April 10: Today on the Conference channel, it’s an Interview in Action live from ViVe 2023 with Lacy Knight, MD, System VP & CHIO at Piedmont Healthcare. As a first time attendee of ViVE, what was he looking to learn more about during the conference? How would Lacy be interested in utilizing AI and data to improve the patient experience? Has his organization established governance around AI and Machine Learning use?
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All right, here we are at VI for another interview in action. And today I'm joined by Lacey Knight with Piedmont Healthcare. How you doing? C? Good to see you. I was thrown by your C H I O title. Yeah, I mean, I guess we're starting to see cm i, o and c h i o pretty u well used interchangeably army.
you know, I think ch i o is considered kind of the next evolution of, that. So it's provider agnostic. So I would say my responsi. Not just to look out for what the physician needs are, but for nurses and ancillary staff, other healthcare groups of healthcare providers. And I do have cmis that work with me where they're focused specifically on providers or physicians and apps and then others that are focused on nursing and so on and so forth.
Fantastic. So, coming into Vibe, is this your first five? This is my first five. Wow. So coming into. What were some of the things that you were focused in on? You're like, I want to hear more about this, or find out more about some of these topics.
So, Piedmont's a large organization, and so we've been doing a lot of m and a activities.
So as we've been growing, there's a lot of restlessness, I think, in the healthcare industry about trying to do things better. So I was particularly interested in understanding, what type of AI and machine learning topics are out there, what companies are doing. Also very interested in learning how people use what I will call maybe the next level of data.
I think Piedmont's a very advanced and data driven organization. A lot of people with expertise in Tableau and we do a lot of training in sport network, but it still requires someone to develop expertise to be able to access the data, which. I could do if I had more time. Right.
So how do you, I don't have a lot of time.
How do you, I don't wanna talk about cloning on this show, but how do you replicate the skills required to operationalize these AI ML tools, how to take the data to the next level?
That's a great question. I don't know that we've done that. What I'm interested in doing is making sure that whatever information is accessible in data, how does it somebody like me get to ask a question and get an answer within a few minutes without requiring time from like an, wiz kid, bi developer that needs to either do the machine learning for me or predictive tools for me?
How can I do the easy stuff by myself? And then let them use the complicated stuff, the same type of thing we're talking about for top of license for clinicians.
So you're looking to put those tools in the hands of the clinicians. Yeah. Is there any conversation in terms of how to engage patients with, or at least take the data from the patients and respond back with automation?
I know. there's clinical staffing shortages. There's, I know I'm, pushing, I'm pushing the envelope here only because that's the type of conference this is. I mean, you're gonna talk to a lot of Lot of people and some of it's gonna be slideware that we just look at and go, that'll be great.
show me something and then we can talk about it. But some of it, like, I'm shocked at how quickly AI is moving right now. Yeah. It just feels like it's moving
Yeah. So the reason I was laughing is because I got a text from a colleague that's here now that asked me, you think there's someone using ChatGPT type function for.
messaging You know, To be able to answer patient messages. And so my first thought was like, I'm pretty sure some doctor somewhere is using it for the complex message just to streamline it. I think that would help, but I feel like it needs to be defined as a bot, so patients understand that maybe there's some simple questions that they don't need to escalate to a physician or a nurse and kind of those algorithms.
I think that would be interesting to do. I do think on the other, end we don't do a great job giving patients a picture of how they're doing in their health journey, so to speak. Right. So, you go to the doctor, there's some boxes to check about whether or not your blood sugar's, okay? Your blood pressure's, okay.
And, you know, exercise more, eat better is like a repeated advice. But it'd be nice, I think if patients could actually take the same type of data, represented Over time and maybe compare it to people like them and how they were doing, you can do that with financial data, like see if you've saved enough compared to your peers in age group.
But you can't do that with your own health data, which I think would be really interesting.
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, it's, it's interesting cuz AI and ml, we just get a lot of pushback from the physicians. Saying, oh, first of all it's not smart enough. And second of all is this replacing jobs? And I think there's a sort of a realization now that it's not going to be the clinician, it's not going to be smart enough to be the clinician.
However, it can do some of those, little things like somebody's walking through a grocery store and they're thinking, Hey, I have this condition. Should I be eating? Yeah. That's the kind of thing that technology can just step in and go, yes, no, here's some of the reasons, some of the considerations, that kind of stuff.
Instead of, we have all these messages that are just barraging the clinicians.
Yeah. Yeah. That's an interesting idea. I don't know, I mean, I haven't seen anything like that. If there's something like that here that helps provide some AI driven personal guidance well, I wouldn't be worried about the clinicians, but maybe the personal coaching.
Right. Well, you know, it's I'll give you our example. And our example is, we do media and we do these interviews and then we'll do the transcripts. And we used to have a full-time writer. Now people are gonna hear this and go, oh my gosh, you got rid of a full-time writer. We got rid of a full-time writer because we now put the stuff we were putting it through chat GT three, now we're putting it through four.
And with three. What we were getting was what I would call a crappy first draft. Like you'd look at it and go, we're not putting this out for public consumption. With four, though, we're getting stuff that we're looking at it going, oh my gosh, this is like really good. And that's what I talk about when I'm talking about how quickly it's moving.
I think we're gonna see entrepreneurs step in there very quickly and go, Okay. We used to have to train these individual models. When you think about like chatbots that we deployed before , we put 'em out there and we train 'em on this very specific thing. Yep. You want, You want an appointment or that kind of stuff.
This thing's been trained on this massive data model and it's answering questions on. Like you can go in and ask it about string theory or you can go in and ask it about diabetes. You can go in and ask it about code. I've had a right code for me. I'm sitting there going, it feels to me like it's moving a lot faster.
Now we're at the . Peak of the hype cycle. It'll sure crash a little bit. Sure. But I think what we're seeing is people are stepping into it and they're going, wow, I expected to be skeptical and now I'm. How is this thing being this, yeah, this accurate? It's kind of crazy. I'm not sure I'm asking a
I'm just, you my Yeah
no, no, no, no.
I think the thing in it goes in my head. So we presented a few of these models, so, there's a dolly one. Yeah. There's a glass one , and I dunno if I should be mentioning all these, but No, it's fine. , But these examples in front of , some of the physicians and they're like, this is really exciting.
We should be figuring this out. How I figure out how we start to introduce some of those things into a large health system when we know that they make errors now. So where are the safe places where we can bring some of these things in? The easy stuff we've, we know people have been doing, job descriptions.
Some email editing responses, those types of things people are doing. Yeah. But as it moves towards the clinical care to save some time, I don't know.
Yeah. It's gotta be so much more careful. I, but I, I will tell you on the, you nailed it before with your, you know, I'm sure somebody's doing this somewhere.
Yeah. When I came in as CIO at st. I remember getting a phone call from Dropbox and they said, Hey, congratulations. You're one of our largest users. We didn't have any contract with them or anything. And I'm like, yeah, and I went back to my team. I'm like, this is what happens when you don't adopt the, when you don't get in front of the cloud.
I know what happens is all of a sudden you're like, oh, we're using it there. We're using it there, and then you have to step back and go, oh, We've gotta, put some, not to slow it down, but Yeah. We've got some governance around it for safety and quality. Yeah.
Yeah. I would love for us to be the faster version so that there's less appeal to doing something outside.
I don't think that'll ever happen with large companies, but anything, in some use cases, if you know that there's a problem that people are going after , you can focus energy to that so that your solution is preferred, either because it's more deeply integrated or it's support. That's one of the things that we were successful with our embedded video visit model,
AI and ml.
Has your health system established governance around its use yet, or
is it So my to-do list in the next few months and frankly, we're just gonna start by just listing everything that we have. Because I'm not sure that everybody knows what we're doing and then trying to figure out some principles
that's where governance starts.
Yeah. And then you look at the list and go, oh my gosh we've got some work.
Yes. I did hear one of the meetings this morning, which I loved I forgot where he was from, but he talked about, they went into projects looking for success factors and then it was a large failure. And so now they've incorporated failure factors into their decision making so that they understand when to quit something.
Yeah, I was like that'll be a good, that'll be a good thing I think for machine learning is to figure out when you stop using it cuz it's not working.
Yeah, that's interesting. One of the things I like is the increased scrutiny that is going on on projects now. Cuz one of the problems I had as a CIO is we had so many projects going on and it's almost like you weren't allowed to say, Hey, is this effective?
Are we actually getting the return? And it sounds like we're finally putting that rigor. On all of our projects to say, let's make sure that we're getting what we think we're gonna get outta it.
Yeah. I haven't figured out how to apply that rigor and then still be agile. Agile and fast and, yeah. Yeah.
So that's something we have to work on.
Fantastic. Lacey, I wanna thank you for your time.
Right, thanks Bill.
Another great interview. I wanna thank everybody who spent time with us at the conference. I love hearing from people on the front lines and it's phenomenal that they've taken the time to share their wisdom and experience with the community. It is greatly appreciated.
We wanna thank our partners, CDW, Rubrik, Sectra and Trellix, who invest in our mission to develop the next generation of health leaders. Thanks for listening. That's all for now.