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Keynote (Rewind): AI in Action - From Buzz to Bedside

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Bill Russell: My name is Bill Russell. I'm a former CIO for a 16 hospital system and creator of This Week Health, where we are dedicated to transforming healthcare one connection at a time. Our keynote show is designed to share conference level value with you every week.

Now, let's jump right into the episode.

Bill Russell (2): Welcome to a special Keynote rewind episode. You know, I've been having conversations about AI in healthcare for years now, and I've noticed something interesting. We've moved from what if to what now. Today, we're diving into how health systems are actually putting AI to work, not in some distant future, but right now, in real hospitals, with real patients. Let's cut through the buzz and get to the bedside.

Let's start with a reality check. When we talk about technology adoption in healthcare, we usually measure change in years, sometimes decades. But something different happened with AI. Let me share some perspectives that might [00:01:00] surprise you.

 ACT 1: Reality Check

Bill Russell (2): so how many times in our history have you seen an 80 year old person who's typical technology growing up was a oh, I don't know, Smith Corona, typewriter say this virtual care thing, it's actually the future. Right? So the cultural change is profound. At Mayo, just to give you a sense of 2020, we had 3% of our visits virtual in January. 95% virtual by April. And now about 25% is the new normal. So sure we're not still at 95. But we've gone from three to four to 25 in one year again. How many times in our history have you seen adoption of a technology that goes up by 5, 6, 7 times in a year?

Think about that - 3% to 95% in three months. But here's the thing about healthcare, speed isn't everything. Trust is. And that brings us to a crucial [00:02:00] point about how clinicians actually think about AI.

Pete Clardy: But what I would say is different about health care and one of the things that's different about how we're thinking about adoption from a clinician perspective is that telling me three sentences about heart failure when there's 500 pages of heart failure information. may actually not make my situation better.

Because if I don't trust those three sentences... I'm still looking at 500 pages of heart failure information I have to sort through. It is the question of establishing trust. It's a question of understanding how new tools work. And it's one of the reasons why the idea that we're grounding you back to the primary data is useful. Clinicians will frequently say, trust, but verify, right? I believe what the ER tells me about the ICU admission, but I'm still going to look at the x-ray myself.

I'm still going to look at the EKG myself. And I think that mindset, you can create patterns for [00:03:00] clinicians so that they recognize, okay. I see what the tool is doing, and I can verify it in this way.

Michael Pfefffer: We have to hold ourselves to a higher standard in healthcare. So we use words like responsible and trustworthy, and those are really important words, but they're Chief Information and Digital Officeralso really hard to do.

Bill Russell (2): While we've been debating the future of AI, something interesting has been happening behind the scenes. Health systems have been quietly implementing AI in places you might not expect - and getting real results. Not in the headlines, but in the operations that keep hospitals running.

Kristin Myers: You know, These two areas can really help us generate revenue and reduce costs and improve operational efficiency and, also improve clinical outcomes. So we have a clinical data science team that builds and operationalizes AI and ML decision support applications. And. Really to improve clinical quality, safety, and the [00:04:00] patient experience.

We're also looking at and have leveraged AI vendors and products in the radiology and imaging space. We also are focused on robotic process automation. And, this has helped us accelerate automation for many of the administrative processes that we have in place. We're also looking at G P T.

Large language models and the G P T integration with Epic for potential usage within the health system. So it's an exciting time around AI and intelligent automation, which we hear about every day.

Bill Russell (2): Revenue cycle, imaging, RPA - these aren't the sexy AI stories that make headlines. But they're working. Now, why is healthcare so complex that we need AI to help manage it? Well, let me share an analogy that really drives this home

BJ Moore: Healthcare complexity itself. Is this off the charts you think about something like a hospital bill? [00:05:00] And I use the analogy. I just spoke at a, at a conference in Austin and I use the analogy of a hotel bill. Right? If hospitals were running hotels, when you checked into your hotel, it would be, well, can't tell you how much a room is going to cost us anywhere between 50 and $5,000.

Depends on how many beds you use, how many towels you use. We'll bill you at the end and you would get this 10 page bill. All right, bill, used three bath towels. He used two wash cloths used the shampoo, but you didn't use the conditioner and your bill is $580. That's what we do in healthcare.

So the level of complexity keeps us from modernizing and then doctors and nurses clinic clinics, hospitals all worked independently. And so there was independent variation does by design worked in the system. So now that you're trying to do things at scale, so you take the complexity and then you take kind of a localization that happened.

[00:06:00] I would say it's those three things that really create the barrier. So Kind of modernizing and simplifying healthcare.

Shez Partovi: So this tsunami of data, in some, digital data, in some ways is facing the clinician Overworked, overwhelmed, facing burnout, we have all kinds of pressures and they have now more data to process. So where we find ourselves in digital transformation is the pace of innovation to create digital data has outpaced pace of innovation to derive meaning from that digital data.

We are in a situation we have a wealth of data and a poverty of insights.

Marty Paslick: So we have a virtual nurse doing admission assessments. Doing the discharge planning and you know this Bill, that, that is a huge lift off the shoulders of these nurses that are bedside every day. objective is, hey, how can we use these technologies to reduce this administrative burden and let nurses practice at the highest level of the, of their technical capability.

Bill Russell (2): So [00:07:00] we've seen AI working in the back office. But what about where it really matters... at the point of care? How are patients actually experiencing AI, even if they don't know it? Let's hear from someone on the front lines.

Jennifer Owens: I think frontline and access is really where the patients will start to feel the impact. First we're starting to focus on how do we open up more appointments to more patients? How do we make sure that patients get in to see their doctors sooner? Maybe they won't feel the direct touch of artificial intelligence there, but as you've got more management of schedules and more effort into the process by which somebody lands on your clinic schedule for that particular day. I think you'll start to feel it. I don't, it's interesting 'cause when I'm thinking about how patients are gonna feel the effects of ai, a lot of it is gonna be kind of diffuse, right? If you're in the outpatient setting, it might look like a reduced time to appointment.

If you're on maybe like a diagnostic journey, maybe it's a reduced time to diagnosis, right? If we're able to take advantage of more lab tests, if we're got, like AI doing a second read on your imaging. Yeah, I'm thinking in the very short term [00:08:00] future here. If you're inpatient, there's a lot of AI that might be brought to bear on your care.

You know, we talked about sepsis risk prediction., we may be looking at some monitoring of hospital acquired infections. We might be looking at, ways to manage floor staffing and, are you at a regional hospital or are you at the main hospital? I think a lot of patients are going to feel a lot of it, but they may not know that they feel it.

Just like there's a lot of stuff that if we brought it onto the market today, it would be called artificial intelligence. It's just not called that because it was developed earlier. You know, I'm thinking about a lot of the innovations in imaging.

CT Lin: in my organization, everyone knows that CTLin ruined healthcare by insisting that users use computers. And and so one way that CTLIN is trying to make up for that is knowing that during the pandemic, as our incoming patient message volume tripled, 350 percent increase from 53, 000 a month to 190, 000 messages incoming per month.

It's an unsustainable work that clinicians start that work after five o'clock, right? You have a full day [00:09:00] of seeing patients in clinic or in the hospital and you go home and guess what? You, on the average, Primary Care Doc has 20 to 50 more messages to deal with every day. And it's an enormous burden and we thought applying generative AI to draft a reply might, in some fraction of cases, handle this volume.

Dirk Stanley: I think another thing that's actually really helpful is summarizing big charts, right? And sometimes you have patients who have 30 year histories with complex chronic diseases, and they're on a million meds, And you're asking a question like, I just want to know, did the person ever have any skin biopsies?

And rather than reading 30 years worth of notes, just being able to ask a question like that's the kind of stuff, even just being able to search for that.

Cris Ross: In radiology, for example, there can be a heads up that this, patient likely has a pulmonary embolism, so let's put that at the top of the list.

They actually put a little exclamation point next to that case so that the radiologists get to it more promptly. And there's a number of sort of calculators and [00:10:00] supporting tools and so on. We're using it to guide radiotherapy for a variety of cancers. We started with head and neck in collaboration with Google and we're moving into other spaces.

So that a radiation oncologist can spend less time sort of treatment planning and more time in other parts of the sort of treatment life cycle.

Bill Russell (2): Now let's talk about something that gets me really excited, AI in specialized care. This isn't about replacing specialists; it's about giving them superpowers. Listen to how these health systems are thinking about the human-AI partnership.

 ACT 4: Specialized Care – AI in Complex Settings

Zafar Chaudry: I firmly believe there isn't a battle to be won. But rather a synergistic partnership to be forged. If you're looking at team human versus whether you are team AI, I don't think they're in competition. I think they bring complimentary strengths to the table. ' cause human clinicians, they offer what?

Empathy, critical thinking in complex situations, a nuanced [00:11:00] understanding of the patient experience. What AI excels in is the data analysis, the pattern recognition, the automation of repetitive tasks, and providing objective insights. So I think the future of healthcare lies in the effective collaboration between the human and AI.

Shakeeb Akhter: I think it's going to disrupt clinical work in terms of, if you look at radiology, pathology, lab, the ability to diagnose, the ability to write a report. Those are things that AI is going to get very good at in the next three to five years. And we just need to figure out, I think you mentioned this earlier, it's not about replacing the human.

It's about augmenting the abilities that we currently have, making us more productive through the use of the AI, while always keeping a human in the loop for making sure it's making the right calls and the right judgment. So, I think I said this to a group of physicians, you know, will not replace physicians.

The [00:12:00] physicians that use AI replace physicians that don't. And that's going to be, I think, true for every role within healthcare.

Brad Reimer: So, we've got that as a direct feed of the genomic information into Epic, into some discrete fields. that we can then play part of the decision aids and those types of things for physicians, BPA alerts. So if we have somebody that went through heart surgery and they were likely to be prescribed Plavix prior to that actually even happening, those alerts and information is going to be in front of the provider as part of their workflow for what they're doing.

Okay. to know whether that patient's going to metabolize Plavix in the right way or not. And it's not something they're having to do to go outside of the system or some other step to go and research that. They're getting it right in front of them as they're going through that care with the patient.

Albert Oriol: I have no doubt that one day we will look back to think about how primitive were we when we [00:13:00] were prescribing drugs and therapies for people without really knowing what constituted their DNA makeup.

Bill Russell (2): So where does all this lead us? After hundreds of conversations about AI in healthcare, I've learned that the path forward isn't just about the technology - it's about how we integrate it into the fabric of healthcare. Let me share some wisdom from leaders who are thinking years ahead.

Seth Hain: I've been spending a lot of time thinking about the way that integration evolves you're a student of the industry, as am I, and the integration of applications was one thing that we saw provide deep value.

Over time, and over the last couple of years here, we have been spending a heavy focus on something we think of as, and describe as the health grid, which is integration of the larger healthcare ecosystem. With the patient remaining at the center, just like they were at the center of our integrated suite of applications that we built.

The health grid gives the [00:14:00] opportunity for that type of integration across sites of care. So it might be dental clinics, which you and I have talked about in the past as an example. It might be specialty diagnostic groups therapeutics. It might be retail clinics. It might be Coordinating clinical trials across these different sites of care as an example.

And I think that network approach and the opportunity that as a patient, as I want to move between those sites of care, but have a consistent view or open up is very exciting. And then I think the layer that will come after that is integrated intelligence. on top of that.

Christopher Longhurst, MD, MS: And the devil's in the details, right? It's how we do it, how we roll this out, how we train, these AI agents or tools, and particularly if they're going to be functioning in unsupervised way, we have to have a level of confidence that these are not going to, introduce [00:15:00] unintended consequences or harm our patients.

And so I think for the short term future, we it's going to be sort of a human in the loop. But I'm fond of, paraphrasing someone else's quote that I'm quite sure we're overestimating what AI is going to do in the next two or three years in healthcare, but I think we're vastly underestimating what this is going to do over the next seven to ten years.

And I really do believe that this is, As important a moment in healthcare is the introduction of penicillin. I think that 10 years from now, the delivery of healthcare will be universally AI enabled, and we're going to see a rapid change in medical legal practice where, operating as a provider without an AI agent I think 10 years from now may be considered to be below the standard of care because we're gonna be able to show that physicians, with augmented intelligence, with AI support can deliver better outcomes than humans alone.

Bill Russell (2): Integration, intelligence, and then... disruption. But [00:16:00] here's what keeps me up at night thinking about all this.

Taylor Davis: It's really hard to say where it's going to go. and so there's the technology piece of this and there's the human piece of this. and both of them are gonna play a role in how does this, how does this take off the technology piece?

We haven't created technology before as humans that has the ability of recursion, of actually creating new technology itself. and that ability opens the door for possible exponential growth in terms of what we're able to do. and so the fact that we have created a technology that can create technologies, that's something that makes people really uncomfortable with, but you're similar.

This is like the iPhone being released. In December, which was G P T 3.5, and then the iPhone eight being released in late March. And that type of progression cycle is pretty startling in terms of where it goes. Let's talk about the human element, bill a little bit. Cause I think this is gonna be interesting and here's a prediction that I'm gonna have fun making.

[00:17:00] we've watched for a long time and there are good reasons why very smart leaders. act this way, but technology adoption in healthcare, because of just the high acuity and the complex, service lines that we run, technology adoption in healthcare oftentimes lags that being seen in other industries, right?

It's just harder to implement and you can't implement, or you can kill people. And, and so there are good reasons as to why it should actually lag in healthcare, but, if a lot of the predictions, and it's interesting, you notice a lot of the stories coming out, right now if you're reading the Wall Street Journal or some of these things, there's a good number of stories where, leaders are saying things like, well, it's not necessarily gonna replace jobs, it's just gonna make our average worker a lot more efficient. and I'm here to tell you, as a business leader, if your average worker is a lot more efficient, that just replaced jobs. So, and I think that really what you're seeing is that you're just gonna have less people, you're gonna be able to [00:18:00] hire. just like I experienced yesterday, I don't need to hire with our new business.

I don't need to hire some programmers that I would've hired last year if we were launching this new business. Yeah. there are some things that, that I'm able to do just with my own programming knowledge as, deficit ridden as it is riddled as it is, I'm able to do those things because I have this kind of really smart technology with me.

We are going to see an American. business over the next three years are transformation in reduction in this, human capital that are doing non-value generating activities or very repetitive activities and even some creative activities. You're gonna see some of this in the film industry or some of the predictions, and I think that with some really good reasons and whole companies are gonna have very different cost structures, so, some of the predictions right now that, and I think that are well founded, indicate that you'll see at least, short term, you'll see some increases in unemployment, but you'll see some companies with, [00:19:00] significantly different profitability, profiles than what they had a few years ago.

And some companies that don't keep up and are gonna be falling under healthcare if we assume that healthcare continues to be a slow adopter. Your healthcare CEOs are gonna be watching what's going on. They're gonna be watching some of these companies that fall behind, and we're all gonna be shocked to say, wow, that company was looking really healthy three to four years ago.

Now they're gone. And they're gone because they weren't able to keep up on this incredible acceleration curve that was going on. you're gonna see some other companies that shed you, the increase their bottom line by two or three x. And, and they were able to do this by harnessing this technology.

And hospital CEOs are gonna realize in, in, I'm guessing, two to four years, they're gonna start realizing, oh my gosh, we're next. And, and so today where you've got a little bit of a, okay, well let's, kind of be a little bit careful. In two to four years, there is gonna be a drum beat of we have to do this and we have to do this really fast.

Because there's a competitive pressure, and then if we [00:20:00] don't do it, we're gonna, we're really gonna fall behind. So two to four years, healthcare is really, it's gonna pick up and it's gonna look a lot different. And so don't kid yourself in the next two years if you don't really see it picking up in the way that you expected to in healthcare.

Closing

Bill Russell (2): You know what strikes me most about these conversations? We're not talking about AI replacing healthcare - we're talking about AI enabling better healthcare. From that 80-year-old embracing virtual care to AI agents helping physicians make better decisions, we're watching healthcare transform in real-time.

The question isn't whether AI will change healthcare - it already is. The question is: are you ready to be part of that change? Because as one of our guests said, in 10 years, practicing without AI might be considered below the standard of care.

Thanks for joining me for this special look at AI in action. I'm Bill Russell, and remember - we're transforming healthcare one connection at a time.

Bill Russell: Thanks for listening to this week's keynote. If you found value, share it with a peer. It's a great chance to discuss and in some cases start a [00:21:00] mentoring relationship. One way you can support the show is to subscribe and leave us a rating. it if you could do that. Thanks for listening. That's all for now..