Latest / CareTalk: Healthcare. Unfiltered. / Closing Healthcare Gaps with Technology w/ David Feinberg, MD

Transcript

Closing Healthcare Gaps with Technology w/ David Feinberg, MD · CareTalk: Healthcare. Unfiltered.

• 0:00
  Dr.
• 0:00
  David Feinberg was CEO of Geisinger and UCLA Health, then led Google Health before becoming CEO of Cerner.
• 0:07
  For the past year, he's been chairman of Oracle Health.
• 0:10
  So let's just say he's got a senior, senior level perspective on healthcare provider systems and information technology.
• 0:16
  Today, he joins us to discuss interoperability, AI, and health equity.
• 0:28
  Welcome to Care Talk, America's home for incisive debate about healthcare business and policy.
• 0:33
  I'm David Williams, president of Health Business Group.
• 0:36
  And I'm John Driscoll, the president of Walgreens Health.
• 0:39
  Have you checked out the Care Talk newsletter lately?
• 0:41
  It's the source for trending healthcare industry news, links to the latest podcast episodes and highlights, and original blog posts that dive deeper into our podcast topics.
• 0:51
  Subscribe today at caretalkpodcast.
• 0:53
  com slash newsletter.
• 0:57
  So Dr.
• 0:58
  David, what in your background as a psychiatrist has prepared you to kind of lead Oracle Health?
• 1:05
  Well, I think the training in psychiatry, we really think of the core training is, it's being able to listen and understand what's going on.
• 1:12
  And then hopefully, I'm actually a child and adolescent psychiatrist, be able to tell a teenager who's using drugs how we're going to get out of this mess, right?
• 1:21
  So, um, I think some of those core people skills have been really, really helpful.
• 1:29
  And, you know, a lot of what you've been working on lately is, is connecting the healthcare ecosystem with information technology.
• 1:35
  Why is that a valuable thing to pursue?
• 1:38
  Well, to me, kind of the core of health care is people caring for people, and it's built on trust.
• 1:45
  And if you have a strong caring relationship where there's a trusted caregiver, you can actually get folks to engage in their treatment.
• 1:52
  They understand it, it makes sense to get better.
• 1:55
  I see technology as a component of that, as really a tool to help in that core.
• 2:01
  Caregiving, but all of us have gone to the doc and it seems like they don't really look at you when they're treating you because they're looking at a computer or they don't have the information or they explain things into you in a way that you don't understand and you just get bad health outcomes.
• 2:15
  It's like this doesn't make sense.
• 2:17
  So for my whole career, that's what I've been trying to work out.
• 2:22
  And at Oracle, the fun part is we got, we kind of got dealt like it's great hand and now we're going to play it where Cerner's part of it.
• 2:30
  Thank you.
• 2:31
  Of course, we have cloud, ERP, supply chain, human capital management, claims processing, clinical trials, but really what that is, is this disjointed health ecosystem that all of us get caught up in, whether you're on the provider side, whether you're a payer, whether you're a patient or a family or community, make all that mess go away underneath and just present it to me, Thank you.
• 2:53
  If I'm a mom caring for someone at home or a professional caregiver sick and for someone in a clinic in a way that's useful and allows me to do that core piece, which is take care of and David, as you and I know, technology leaders have been saying for a generation, meaningful use this E.
• 3:12
  M.
• 3:13
  R.
• 3:13
  The connected health system, all the big tech folks and the smaller, uh, tech, uh, uh, evangelists have been saying that technology is going to save health care for a generation.
• 3:24
  What's different this time?
• 3:26
  Well, there's a bunch of stuff that I think is actually different.
• 3:28
  First of all, We've now digitized the record.
• 3:31
  So no one got treated during COVID.
• 3:33
  And they said, Oh, we can't find your chart because Dr.
• 3:36
  Smith took it.
• 3:37
  No pharmacist said, I can't read this prescription because your doctor has bad handwriting.
• 3:41
  Now you might say, why did it take us 40 years to get there?
• 3:44
  But it was a slog to kind of digitize the record.
• 3:47
  And now that it's digitized, there's a few things that I think make it different this time.
• 3:52
  One is actually you brought it up COVID.
• 3:55
  Like when you think about it, we gave everyone in the world.
• 3:59
  A kind of public health one on one lesson, like people now know what bend the curve means, vaccines, people even know what anti vax means, right?
• 4:07
  And so we spent a lot, what we, we lived through this experience, we understood it health equity better.
• 4:13
  And I mean, we meaning the whole general population.
• 4:16
  So that's one.
• 4:17
  The records digitized that the things at a breaking point, you know, in my role now, I have this opportunity to go all over and whatever health system I talked to, whatever health system I talked to their number one issue is staffing.
• 4:31
  I mean, it just got into this breaking.
• 4:34
  So that's a potential opportunity, right?
• 4:37
  When you think about now, can tech come in?
• 4:39
  And I think this time, and I hope our organization has done this.
• 4:43
  We're coming in and trying to be much more humble.
• 4:45
  This isn't about, here's an app, you stupid people in healthcare, if you just use this, everything will get better.
• 4:51
  But rather, here's an open platform that's going to be connected, that's going to be built with normal kind of user interface, because I don't think any of our stuff has that.
• 5:00
  That actually makes it easier for you to take care of people.
• 5:02
  So that, that's why I'm hoping.
• 5:05
  That it's better this time.
• 5:06
  It's also the cost.
• 5:07
  Again, that's another national international thing.
• 5:09
  I hear we can't just keep going up.
• 5:12
  It means we won't have schools or wage increases jobs.
• 5:16
  If the only thing in this case the United States does is produce health.
• 5:20
  It's insane.
• 5:21
  especially with our poor quality.
• 5:23
  So I think there's a lot of factors that we can get into AI and all that stuff.
• 5:27
  But I think fundamentally it's, it's a breaking point.
• 5:30
  The record got digitized.
• 5:31
  The tech is good.
• 5:33
  And the populace says, Hey, I want to tell a health visit.
• 5:36
  I had that a year ago.
• 5:37
  It makes sense in this situation.
• 5:41
  John is talking about, uh, you know, some some jargon, some industry terms like meaningful use, which came out of a federal requirement out of the High Tech Act.
• 5:49
  And more recently, federal government's been working on interoperability frameworks.
• 5:53
  Are those important?
• 5:54
  Are they making an impact?
• 5:55
  Is that one of the, is that going to move us forward, hold us back?
• 5:59
  I mean, to me, those are mumbo jumbo words.
• 6:02
  So, you know, Cerner, we're the largest electronic health record company in the world.
• 6:07
  Much bigger than anyone else.
• 6:09
  And when we're done with the VA install in the United States, we'll also be the biggest in the United States, right?
• 6:14
  And what I say to the team is that means we have more of grandma's blood sugar than anyone else.
• 6:19
  Let's get down to really what this means.
• 6:21
  When you say words like interoperability or meaningful use.
• 6:23
  We got to make sure that blood sugar is accurate, that it's secure, that it's getting to the right place, that we understand it's more than blood sugar.
• 6:31
  It could be about food and grandma's pantry, right?
• 6:34
  That's actually the business we're in.
• 6:36
  And then we cloak it in these words around meaningful use and interoperability.
• 6:39
  But hey, we all have grandma, so we all had it.
• 6:42
  Um, and.
• 6:44
  We don't want their thing messed up, right?
• 6:46
  That's what and we want it to be coordinated.
• 6:49
  And sometimes grandma goes here for Karen.
• 6:51
  Sometimes she goes there.
• 6:52
  That's got to go around with grandma.
• 6:54
  So that's what to me this means when we say interoperability.
• 6:59
  Do we understand the social situation that a person's living?
• 7:03
  Are we bringing in some cases?
• 7:04
  Very important claims data.
• 7:06
  Do we understand the disparate parts of the medical record and then are we giving it to the caregiver in a way that is useful for them to kind of do their thing?
• 7:15
  How, when you, when you say, because there is a crisis of staffing around the world and we've got a demographic boom.
• 7:23
  And an aging population across the entire industrialized world, and we're not making enough doctors and nurses fast enough.
• 7:29
  How could technology, historically technology has added tasks and added work, and to your point gotten in the way of the relationship between doctors and nurses.
• 7:39
  So arguably we've gone backwards.
• 7:42
  Why is it?
• 7:43
  What's going to happen now that's going to allow us to move forward and how do, how, how do you, how do you square that circle given our, our, our, our, our really terrible history in terms of implementing tech in health care?
• 7:56
  So, uh, I think it was last month we were in Vegas for Oracle Cloud World and I got to demo our new clinical digital assistant.
• 8:05
  Now this has been built and will be live in customers this year.
• 8:08
  So this is in make believe stuff.
• 8:11
  But here's what happens.
• 8:12
  I pull out my mobile phone.
• 8:14
  I say, Hey Oracle, tell me about my next patients.
• 8:16
  Read patient Julie Smith to me, knock on the room, put the phone down.
• 8:21
  Julie had already agreed to ambient listening.
• 8:23
  I go through my whole exam, talk to her.
• 8:25
  How's the pneumonia?
• 8:26
  Hey, it looks like you're Uh, uh, hemoglobin A1Cs have gone up.
• 8:31
  Hey, Oracle, show me hemoglobin A1Cs.
• 8:33
  I actually got prompted because Julie had food insecurity.
• 8:36
  We just talked.
• 8:37
  Now, as a psychiatrist, I had to practice using the stethoscope, but I didn't pretend and listen to Julie's lungs.
• 8:42
  And when I was done, I said, Hey, thanks, Julie.
• 8:45
  And the metformin got increased.
• 8:47
  The ATCs got, all of it got done.
• 8:50
  And I never, and we set it up this way, looked at the computer.
• 8:53
  Right.
• 8:53
  The computer was actually at my back the whole time I was looking at you.
• 8:57
  So you could talk about Gen AI and all kinds of fancy stuff.
• 9:01
  The technology allowed me to get back to being a doctor.
• 9:06
  And when you talk about staffing and people getting burned out, if we're really able to get people back to the part that feels so good.
• 9:13
  Or when you talk about a nurse who spends 50 percent of the time at the terminal, 30 percent of the time looking for supplies and 20 percent of the time nursing, if I can eliminate that terminal time.
• 9:22
  I mean, we didn't go from so many clicks to less clicks in my demo.
• 9:26
  We went to zero clicks, right?
• 9:28
  If we go to zero clicks for the nurse, goes in, sees the IVs infiltrated, ambient listening, captures everything, including the phlebitis and documents the nurse's note.
• 9:37
  We've just doubled the amount of nurses that we have because they're now no longer connected to the computer, right?
• 9:43
  So from a staffing standpoint, I think one thing that the technology can do is kind of free our people up to do what they want to do, as opposed to you said, John.
• 9:52
  The technology has kind of pulled them away from what they want to be a doc anymore.
• 9:58
  I don't want to be a nurse anymore.
• 9:59
  I don't want to go into this.
• 10:01
  You know, um, I think it's our time and I don't think it's just us doing this.
• 10:05
  I think the industry is doing this.
• 10:07
  And, um, to me, it's fantastic.
• 10:09
  I mean, we got to get this one right.
• 10:13
  John, let's take a second to thank our sponsor, Digital Diagnostics.
• 10:17
  Everyone should have access to ethical, dependable healthcare.
• 10:20
  Digital Diagnostics guiding mission is to make a positive impact on individual patients and the healthcare ecosystem through the integration of point of care diagnostic AI.
• 10:29
  Providing access to high quality AI diagnosis is helping to transform healthcare delivery, focusing on the diagnostic process for the individual to ultimately transform patient outcomes across healthcare.
• 10:40
  Check out digitaldiagnostics.
• 10:41
  com to learn more.
• 10:43
  Digital diagnostics, AI, the right way.
• 10:47
  So, so, Dr.
• 10:48
  David, there's a lot of conversations about AI.
• 10:51
  There has been conversations about AI in healthcare since the 80s, since I remember when I was in college and graduate school, the Marvin Minsky model, and then there were the, the deep neural networks.
• 11:01
  But it does feel like...
• 11:03
  This time is different around generative AI around the massive compute available.
• 11:09
  And frankly, to your point, the availability of the availability of structured relevant data.
• 11:14
  How are you thinking about leveraging all of that access to data that you have at Cerner and Oracle, the tools you have to analyze it at Oracle and the opportunities to kind of generate it?
• 11:27
  Novel solutions at, you know, at at will.
• 11:30
  Yeah.
• 11:30
  So I'd say on one hand, I'm like wildly excited.
• 11:34
  You know, on the other hand, I'm extremely nervous, right?
• 11:37
  And so let me kind of go over those.
• 11:39
  So, first of all, I think a lot of people say, oh, we're going to do a, you know, for things like computer vision.
• 11:44
  It's definitely better than I think human performance.
• 11:46
  In some cases, human loop is always ideal.
• 11:49
  But a lot of people say we're going to focus on the non clinical things first, like throughput in the ED before we get to, you know, improving diagnostic accuracy.
• 11:58
  Throughput in the ED is a clinical intervention, right?
• 12:00
  Because if you're doing CPR in the ED, because you couldn't get a bed upstairs, like the emergency, the emergency department.
• 12:06
  Yeah.
• 12:07
  Yes.
• 12:07
  The emergency part.
• 12:08
  So all, everything in healthcare, in my opinion, is clinical.
• 12:11
  The billing, the coding, it means does the person actually get their Approval from the insurance company for the medication.
• 12:17
  So I get that talking about it separately, but I think anything you're doing in health care fundamentally affects the patient.
• 12:23
  So why am I excited?
• 12:25
  Why am I so nervous?
• 12:26
  When I was at Google with actually Cerner in the NHS, we tried to see if we could use AI to improve the diagnosis of acute kidney injury.
• 12:38
  in hospitalized patients.
• 12:39
  And so the beginning was an app, no AI, but a nice app told the rapid response team, this patient's creatinine is going up.
• 12:46
  Hey, come and see the patient.
• 12:49
  Just good user interface, no AI.
• 12:51
  It went from a four hour time to diagnosis to 14 minutes.
• 12:55
  There was a 30 percent decrease in cardiac arrest and a 60 percent decrease in the cost of care, like mind blowing.
• 13:02
  Then we said, okay, let's do AI.
• 13:04
  So instead of just looking at creatinine, the measure of kidney function, We trained the model on 600, 000 variables per patient times 70, 000 patients.
• 13:14
  So great training model.
• 13:16
  We did this with the VA and instead of four hours and we've got it down to 14 minutes, it now goes to negative 48 hours before any signs or symptoms.
• 13:28
  This patient is no sign or symptom that there's acute kidney injury.
• 13:33
  The computer with 90 percent accuracy says this patient's going to end up on dialysis.
• 13:38
  Like, it's mind blowing stuff.
• 13:41
  So you go, that's why I'm super excited.
• 13:42
  Like, this is like a new field of anticipatory medicine.
• 13:46
  Here's why.
• 13:47
  And you found that probabilistically that was accurate?
• 13:49
  It wasn't just, it wasn't just, it wasn't just a retrospective, hey, we're verifying what we already know?
• 13:54
  No, no, no.
• 13:55
  We, we trained the model and then gave it new patients and they hit it 90 percent of the time.
• 14:00
  48 hours before it normally would take four hours.
• 14:02
  If you did go UX, you got it to 14 minutes.
• 14:05
  Okay.
• 14:06
  But we trained it on the VA.
• 14:09
  Well, that's 93 percent male.
• 14:11
  Guess what you think doesn't work as well on females.
• 14:15
  So as we train these models.
• 14:17
  That actually improve performance.
• 14:19
  Let's make sure that when we use them, they come with a warning.
• 14:23
  It's been trained in this situation.
• 14:25
  It probably works pretty well.
• 14:26
  We're not sure in this situation and please don't use it in this situation, right?
• 14:30
  Because we don't want to make gaps in care, health equity, um, issues that we know are built, unfortunately, into our health system.
• 14:38
  We don't want to hardwire them, just keep making them worse.
• 14:42
  So I am excited because I think it can really augment human caring.
• 14:48
  And at the same time, I'm nervous because I think it could actually make some of the bad stuff we do even worse and, and could be used in the wrong situations.
• 14:59
  So on the AI, it sounds like there's, you know, pluses and minuses from your standpoint or things you're worried about.
• 15:04
  There are a number of folks in the AI community itself who've taken it a lot further, not just to say it may reinforce bad things that are happening, but may unleash all sorts of forces that we can't predict and lead to the, uh, the end of, end of the human race.
• 15:17
  Are you, do you have that concern as well?
• 15:20
  And is it a completely separate one from what you were just describing about, you know, reinforcing existing biases?
• 15:26
  So, um, I'm spending a lot of time thinking about this one.
• 15:29
  And, and I think about it specifically around healthcare.
• 15:33
  Um, if you look at our current healthcare system, let's just talk to the U S where we're getting close to 20 percent of GDP and, um, black women are dying at, you know, two to three times the rate of white women in childbirth, even if they're not poor.
• 15:48
  Like, it's amazingly how, it's amazing how much we spend and how bad our health is.
• 15:54
  So, I'm of the belief, and I'm talking healthcare now, I'm not talking military or anything like that.
• 15:59
  I think for healthcare, these technologies can really, really up our game, and let's be super thoughtful about it.
• 16:06
  But I, and we may need to contain them in certain ways, I'm certainly, we need to regulate them.
• 16:12
  But we have an opportunity.
• 16:13
  The whole point of digitizing the record was not just fixing legibility and making sure that the doctor had a chart in the ER.
• 16:21
  This is magic we can put on top.
• 16:23
  So let's make sure we capture that in the right way.
• 16:27
  Um, I think it's a huge opportunity to really make care more equitable, more cost effective, more culturally sensitive for us to really become a learning organization to learn from this.
• 16:38
  You know, hey doc, We've seen 10, 000 cases just like this and no one has ever prescribed that medicine.
• 16:43
  Are you sure you want to do it?
• 16:44
  I mean, there are so many errors in healthcare because humans are human, that we have the ability to augment that with really, really powerful tools, again, that are used appropriately, that were thought about, that we continue to evaluate.
• 16:57
  So, so I'm bullish on it.
• 17:00
  It's, it's a really interesting thought.
• 17:03
  David, I mean, I, I sort of share your concern about some of the Feral models that the large language models that have been put out without any regulation or controls, but to leverage the power of compute to actually ring fence, stupidity and error and racism.
• 17:19
  I mean, because it's really the math on, on, on.
• 17:23
  Maternal mortality for women of color across all income levels, it's, it's, it's a crime.
• 17:28
  And to think about the, the computer as your, as, as a tool to protect, you don't actually have to get into the area of unknown novelty.
• 17:38
  Uh, you can actually work, work to use it almost as, as a warning system for what's, for what we already know is right and wrong.
• 17:44
  That's a really interesting frame.
• 17:47
  Yeah, I, I, I, I mean, it gives us a huge opportunity to, in a sense, close those gaps.
• 17:52
  Yeah.
• 17:54
  When I asked you about interoperability earlier, you, you pushed toward, you know, grandma's sugars and which I take as sort of part of a theme.
• 18:01
  Sounds like a song.
• 18:02
  Did I hear it wrong, John?
• 18:04
  As part of the theme about, uh, you know, putting patients and families at the, at the center of care.
• 18:09
  Does, does AI help to do that?
• 18:12
  Are these the sort of things that we can, that we can do better with AI or is there a completely, you know, non technological approach that's needed?
• 18:18
  For that.
• 18:19
  Yeah, I think it could certainly help families.
• 18:21
  I mean, when you think about it, if you're looking on your phone at your labs through some portal, people don't really want to see their labs.
• 18:30
  What they want to see is their bad cholesterol or when is my gyne appointment, right?
• 18:35
  So AI can help us translate medical ease into what, what am I, what do you think I'm searching for?
• 18:41
  What am I searching for?
• 18:43
  And I don't have to think your way, you know, Hey, this thing came back from the pathologist that I have something in my prostate.
• 18:50
  Is that good or bad?
• 18:51
  Well, that's where AI and especially gen AI can, I think, really help.
• 18:55
  In that translation.
• 18:56
  So from a consumer facing standpoint, it's really, really good.
• 19:01
  I think help make health care more understandable, right?
• 19:05
  Because right now it's particularly confusing.
• 19:08
  I mean, just think about any of us who get EOB.
• 19:11
  I'm an MD.
• 19:12
  I got an MBA.
• 19:14
  I've run health systems.
• 19:15
  I get EOBs from my own health system and I have no, I say to my wife, what do you think this is?
• 19:20
  So talk about someone who has less health literacy than me.
• 19:24
  We have created a system that is totally incomprehensible.
• 19:27
  Well, that's actually what AI is pretty good at.
• 19:29
  Like take all this stuff and make it make sense for me in my language.
• 19:33
  And, uh, I think that's a big potential.
• 19:37
  I think EOBs are everybody's favorite.
• 19:38
  Yeah.
• 19:39
  What I like about it is it says, okay, this is not a bill.
• 19:41
  I said, okay, I got that now.
• 19:43
  What else is maybe that can work out like what all the other things that isn't and then I'll figure out what it is from that, but they just say it's not a bill, but it doesn't at least nor is it nor is it terribly predictive of what that bill is going to like it's it or descriptive of what it is.
• 19:55
  You know, one of the more interesting applications I, I, I heard about recently, uh, Dr.
• 20:00
  David is that the AI was more effective at writing sympathetic apology letters to people around.
• 20:08
  Basic mistakes that hospitals and doctors have made than the hospitals and doctors, you know, complaints department or that there is a, there's nuance here around automating and simplifying.
• 20:17
  But I thought it was remarkable that it was easier for the computer to be heart centered in an apology than it was for a department head.
• 20:26
  I think we're, we aren't untoward.
• 20:28
  We are in a weird, weird territory.
• 20:31
  The computer is probably a little less defensive than the person.
• 20:35
  Exactly.
• 20:35
  Hey, sorry that we, we sent you the wrong result on your biopsy.
• 20:41
  Yeah.
• 20:43
  So, you know, we had COVID, we had, uh, you know, 2019 come conveniently 101 years after the 1918 flu outbreak.
• 20:51
  These come every hundred years or do you expect a little less than a hundred years or a little bit more?
• 20:56
  And if a new pandemic were to hit.
• 20:59
  In 2024, will the system be ready?
• 21:02
  Well, let me be a little edgy.
• 21:04
  I'd say we have another pandemic going on.
• 21:07
  If you really look at, at illnesses that are driven by a lack of access to good food, unsafe housing, poor education, difficulty with transportation, um, wage stagnation, all of those things drive multiple times, things like diabetes, hypertension, high cholesterol, kidney disease, and.
• 21:29
  If we want to decrease the health care cost in America by half overnight, we just need to address those things, right?
• 21:36
  And so what we've done is create a system to try to fix those things once they got bad as opposed to going upstream.
• 21:43
  And when you play this out, we're not even good at fixing those things.
• 21:46
  But if we were to really to go upstream and treat what I'm describing as this current pandemic of foodborne illnesses and lack of opportunities in communities and poor access to education, um, we know those are drivers of health.
• 22:02
  We just know that loneliness kills people and, um, living in neighborhoods where people are shooting one another leads to stress.
• 22:10
  And medical and mental conditions like this is not new stuff.
• 22:13
  So I, as opposed to worrying about what's coming in 100 years, why aren't we talking today about these issues addressing these issues?
• 22:22
  Not only are they nice from a hey, I'll feel better in a human standpoint.
• 22:26
  They would actually solve the financial issue of the healthcare system.
• 22:30
  And I think a big way of getting is to say, how, how can we stop talking about moving at a fee for service to a fee for value and actually move to really a value based system, because now all of a sudden.
• 22:43
  Those things matter.
• 22:44
  I don't want you going to the E.
• 22:45
  R.
• 22:45
  Because you got chest pain and it's from a panic attack.
• 22:48
  I want to treat your panic attack.
• 22:49
  We're in the current system.
• 22:50
  No one really cares.
• 22:52
  You know, David, I think you and I probably agree that when people ask, what's the one thing you could do to fix health care?
• 22:57
  I said, fix the farm bill and stop subsidizing the things that are killing us.
• 23:03
  And, and, and, and start, start, start supporting the things that actually would provide healthy people and ensure that everybody is, is known as food insecure.
• 23:11
  But I wanted to go a slightly different direction.
• 23:14
  We'll hit the farm bill later, David, don't worry.
• 23:16
  Okay, good.
• 23:17
  Thanks.
• 23:17
  Which, which is, which is how do you, can the machines with this epidemic of loneliness, with your unique perspective as a Mandarin.
• 23:26
  Of one of the great tech companies, while also having the background of a psychiatrist, can the machines help us with this epidemic of loneliness?
• 23:34
  And if they can, how?
• 23:37
  Well, I think they're going to be really good.
• 23:39
  And you kind of talked about that, about the apology note in really understanding emotions and seeing, Hey, this is how I, this is how I want my robot to talk with me.
• 23:50
  And maybe you want to have a different kind of relationship with your robot or your AI companion.
• 23:55
  Um, so it feels a little creepy to me.
• 23:59
  Yeah, totally.
• 24:00
  There's still something about a person caring for you.
• 24:04
  Um, and, but I think it could augment.
• 24:07
  And, and so, I mean, I sometimes give an answer on.
• 24:11
  You know, is a I going to replace doctors?
• 24:13
  You're talking more about companionship, but but I say a I is not going to replace radiologists, but radiologists that don't embrace a I will be replaced.
• 24:22
  So I would give a similar answer around this loneliness.
• 24:27
  Issue.
• 24:27
  For example, I think that computers and tech, um, in some ways can make you even more lonely.
• 24:36
  Like you can get kind of lost in your social media.
• 24:39
  Never looked up.
• 24:41
  Um, if used appropriately, we got to figure this out.
• 24:44
  I don't know that any of us have the answer.
• 24:46
  I think can help.
• 24:47
  Um, address some of these things, but I still think from a human standpoint, we're people that need to be with other people.
• 24:55
  And we saw that during COVID, like just being isolated, we just saw dramatic increases in mental health.
• 25:01
  So maybe the tech can help bring us together, augment how we're together and can help me be with you in a better way.
• 25:07
  But I still think it's.
• 25:08
  I still think we're fundamentally in people business.
• 25:12
  Well, that's a good note to end on.
• 25:14
  That's it for yet another episode of Care Talk.
• 25:17
  Our guest today has been Dr.
• 25:18
  David Feinberg.
• 25:19
  He's chairman of Oracle Health.
• 25:21
  I'm David Williams, president of Health Business Group.
• 25:25
  And I'm John Driscoll, the president of Walgreens Health.
• 25:27
  If you like what you heard or you didn't, please subscribe on your favorite service.
• 25:30
  And thank you, David, for joining us.