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The 229 Podcast: Building AI-Powered EHR Tools and Educating Staff with Dr. Stephon Proctor

Stephon : it really got the juices flowing for a lot of people to see just how useful embedding something like an LLM into your EHR could be in terms of an opportunity.

Bill Russell: My name is Bill Russell. I'm a former health system, CIO, and creator of this Week Health, where our mission is to transform healthcare one connection at a time. Welcome to the 2 29 Podcast where we continue the conversations happening at our events with the leaders who are shaping healthcare.

Let's jump into today's conversation.

All right. It's the 2 29 podcast and today we are joined by Stephon Proctor PhD with children's Hospital of Philadelphia, associate Chief Health Informatics Officer for Platform Innovation. Stephon, welcome to the show.

Stephon : Hi, bill. I'm honored to be here.

The clinicians, at least in a test environment to be able to interrogate the medical record and get a generative AI response, which I thought was really fascinating and worth discussing. I'd love for you to talk about that. And we are gonna talk about that a little bit. But first what is a full stack clinical informaticist, which is what your profile says.

Stephon : So for those who don't know, an informaticist. At heart is really good at bringing the clinical and the technical together. We're really like the bridge builders between that and there's different levels to which we have expertise in things like the technology end. And for me, because I really enjoy technology, I'm really self-taught in everything.

And so in many ways, I feel like I have a very end-to-end view of what happens in the office all the way down. You know, to the database level and everything in between, which makes it really helpful. So if we are designing a new clinical interventions or new tools, I have an understanding of where all the pieces need to connect to make it work.

Bill Russell: How the data resides, understanding the workflow, understanding how the clinician is going to utilize the system, gives you a distinct. Viewpoint of how the data's flowing through the system, and then how to get the data back out of the system.

Stephon : So we're doing a lot of things. On one hand, we are implementing a lot of the generative AI products that our EHR vendor Epic is developing. And on the other hand, we've started to do some prototyping and experimenting with creating our own apps. The LinkedIn posts that you're talking to.

Was about an app that I developed called Chipper. So to kind of take a step back like most organizations that were giving their clinicians access to generative AI tools, we had our own instance of open ai. We called it chop, GPT. It was a web-based way for you to ask clinical questions in a compliant environment.

That's really good design. And so for a while, instead of copying and pasting things in between. Chop GPT and Epic. You know, I had that, you know, you remember like those infomercials, like there's gotta be a better way. And that's exactly kind of where it started. And so I started to poke around and I had known for a while that with other vendors and tools that we have that you can embed tools or even into Epic.

So then that got me thinking, well, could we embed chop GPT into Epic? And if so, what would that look like? And then I started to expand that further, that if I could get that in just as a webpage, but that's not gonna be good enough, right? Clinically, I want it to be aware that when I ask it a question, it knows about the patient that I'm talking about, not that I have to kind of provide that information.

And so I think that was kind of another. Aspect of technology that it's come so far that someone like myself who, you know, I'm a dabbler, I like to dabble in everything and AI really like ups the level to which people who really kind of have a general understanding of technology can take it to the next level.

And then we thought, well, could we go beyond that? Could we integrate it with other sources? Right? Could we have a query? Things like an external database PubMed to help us look for medication and so on. And really what was amazing from that post is just to see the huge reaction that we were getting from a lot of people who o e, really wanted something like that.

And two, it really got the juices flowing for a lot of people to see just how useful embedding something like an LLM into your EHR could be in terms of an opportunity.

Bill Russell: Well, I would assume that a lot of people are. For Epic or Cerner to roll out something like this? what kind of timeframe do you think that's

Stephon : the recent UGM Epic actually announced that they were gonna go live with a similar type of interface where I think they were gonna call it live Insights or conversational search.

It is pretty impressive. I think it is something that I think many of us were surprised it already wasn't something. But clearly they had al also been thinking about that as well. I'm also not surprised that if other vendors, you know, especially the ambient scribe vendors would start to do the same thing as well.

'cause really, ultimately what I did wasn't groundbreaking in terms of technology. I really connected the dots that were already there.

You know, the question is, how are you gonna provide the context for the LLM to make sure that it can find the information it needs, and then how are you gonna provide the guardrails? So talk a little bit more. Yeah, I mean, is it, are you using like a rag backend? . yeah.

Stephon : Yeah. So when the application is loaded. You know, smart On Fire is providing information about the patient, like who they are and everything like that. And then every time you query it, essentially what the LLM is doing is looking at what the user typed and then using a series of tools to try to figure out, well, this person is asking about vitals.

Let me do my vitals tool and then fire a query and then come back with what that person's looking for. So that's really how it's getting that information. So it's always a series of instructions. And then the LLM. Grabbing that information directly from the patient's chart.

Bill Russell: Yeah, so that's interesting.

This tool does essentially a basic or, I mean, not a basic, but it does a standard query of some kind looking at the data set and returns to the information. How do you provide the guardrails to make sure it doesn't. Get outside the lines. I guess

Stephon : even when I spoke with Epic, they also had the same concerns as well.

And that is something I think we're still, US and other organizations are still trying to figure out is what are those guardrails? what are the. Things that you're trying to prevent and then how do you respond to that? One of the biggest areas that we still are exploring is how do you prevent some of this data from going out, right?

Because like with many of us, you are required to have something like a, BA, a business associated agreement. To make sure that you know the information that you're sharing with patients, that vendor is gonna protect that information. So we haven't solved for that yet. I also spoke with epic as well and just to try to see what they're thinking about as well.

And that's general, that's not specific to this, right? You or I could use ChatGPT and you know, put in information and probably get medical advice. That's not helpful or inaccurate. So that's a general consideration that. Everyone has to think with about, with LLMs, but really at the most basic layer, you wanna be thinking about privacy of our patient information.

Bill Russell: I was gonna talk about adoption and workflow integration, but at this point it sounds like you have a series of champions that are utilizing this tool or really testing the tool at this point, what, how many people are utilizing it, how many people are looking at it, and just getting a feel for how it works.

Or do we wanna develop the app for a very specific use case. This is often a debate you see within, do you you know, let's the vendor take care of something that's general, which Epic tends to do, or do you focus on a very particular use case that you know for the time being they're never gonna focus on Right.

It's a certain subpopulation or a certain workflow that is very specific and only affects a small percentage of users.

Bill Russell: from a generative AI standpoint, so you have the web interface that people can copy and paste and it's HIPAA compliant and protected. You know, how much usage does that get at a health system

So at Children's Hospital of Philadelphia, we have what we call a school of ai. And so we are actually doing in-person and virtual workshops to teach our employees what is generative ai, and then how to use it for clinical and nonclinical use cases.

Bill Russell: what does that curriculum look like and what kind of people are coming to that course?

Stephon : Yeah, so we actually just had a hackathon a couple of weeks ago, and it was broken up into three groups. We had operational we had researchers and we had clinicians into that. So we're really trying to be very broad because as you know, generative AI can really focus on a lot of different workflows, so we're keeping it very open.

Bill Russell: what do you focus on right now from a clinical informatics standpoint?

Stephon : Yeah, so implementing or discovering and implementing and evaluating epic's generative AI tools. So we have about nine in various stages of being enterprise being deployed across the enterprise to being in small production pilots.

We were one of the first organizations to go live with the in baskets, AI drafts or art. And then from then on we started to expand into the note summarization for inpatient, outpatient. We're also working on ambient scribes and finding a lot of success with that. So that's really where a lot of my focus has been on more recently is just looking at each of these use cases and evaluating them and seeing where it works.

Bill Russell: Yeah. What's it like going to UGM and then coming back from UGMI? I mean, you're talking about nine they rolled out what, 150 each year for the last two years?

So it is a great deal. As you can tell, they are just looking for every opportunity to put it into a variety of workflows which is really exciting. At the same time, it's coming really fast, and so in some ways you have to kind of decide how much can you keep up with that pace.

Bill Russell: is the demand more a pull from the organization or are is the is the organization, does it have a governance group that's looking at it saying, you know, these five are the next tools that we wanna see implemented?

Stephon : So from the top down, we have a lot of support. You know, even within our strategic plan AI and automation is a pillar of that. So it's great that we have that support there. And then in terms of governance, we do have an AI governance group. And so all of our AI features need to be presented there when, where we're getting feedback on how to construct the pilots, how we're gonna monitor it.

Piloting that. Other times we have to wait for that feature to be maybe piloted with another organization before we can step in line and say, we wanna look at that feature as well.

Bill Russell: I I do wanna come back to adoption, you know, is it, Sort of assumed if you go to work at Children's Hospital of Philadelphia that we're an innovative organization and we're gonna be rolling out new things.

Or do you, or, I mean, how do you get gain adoption? How do you make sure that you don't overload the clinicians with, I don't know, with too many things?

So once we started to move through that and maintain that, it was really inevitable that. AI would be another layer of features that we are looking at. On top of that you bring up a really good question about this overload though, right? If people feel like there's AI in too many different areas, you know, , is that gonna lead almost negatively in, in some way, right?

Bill Russell: The promise of technology is that eventually it's gonna fade into the background and be invisible. And we're essentially going to get to a point where we're just talking to a computer and yeah, there's new features and things happening, but we don't even know that there's a new tool that's been introduced.

I'm curious when you guys, so you, I mean, everybody I know at CHOP is just fun to talk to. You guys are on the cutting edge of a lot of things. I was wondering, when you guys sit around and talk about what the future. Might look like for a clinician at Children's Hospital of Philadelphia, say three years from now.

I mean, is there how do you describe that? What do you think it might look like?

Right? Do we use, do we have microphones that are available? Do we have cameras that are available? In a sense that, to which the room is in a way. Digitally enabled for new technologies such as ambient or maybe in the future when you start to have AI that starts to. Not only hear which is where we're at now, but see right.

Could you imagine that? You know, clinicians, and nurses are walking into a room and there's a lot less of interacting with the computer and interacting with the patient and having the technology take care of a lot of the documentation or teeing up of the orders that you're already doing in the system. So I hope is what you start to see is.

So I think that's what we're gonna start to see is. A lot less friction of you feeling like you have to manage the EHR and move between screens and all of that, but that a lot of that is done with you and then you're, you know, approving that and spending a lot more time in you know, direct patient care.

Bill Russell: Stephon, I wanna thank you for your time and thank you for getting out there on the edge with this stuff. I appreciate it. Yeah, love the experimentation, love seeing where you guys are taking things and others. I think that community epic the people outta Stanford and the stuff that you're doing is moving things forward.

It's gonna be fun to watch in the next couple of years, certainly. Thank you. Thank you.

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