Building Value Through Real-World AI and Smart Technology Adoption

Q: Hi, JD. How are you doing? It’s great to have you on the podcast. Awesome. So JD, as you might be aware, this is The Big Unlock podcast, which was started by the founder of Damo Consulting, Paddy Padmanabhan. We’re now in Season 6 and north of 160 episodes. We’ve come a long way since this podcast started.

I’m Rohit Mahajan, Managing Partner and CEO of BigRio and Damo Consulting. Super excited to have you as our guest and looking forward to diving into some topics. Would you like to start with an intro?

JD: Sure thing. I’m JD Whitlock. I’m the Chief Information Officer at Dayton Children’s, a small pediatric health system in southwest Ohio. I’ve had a pretty long career in healthcare IT—30 years now in healthcare. I’m a retired Air Force healthcare administrator.

I’ve also spent time in larger adult private sector systems like Bon Secours Mercy Health, where I focused a lot on data and analytics. Now, as CIO, I do a little bit of everything IT at Dayton Children’s.

Q: That’s great to know, JD. A couple of questions—just curious. What attracted you from being in the military, in the Air Force and Navy, into healthcare, where you’ve stayed for a long time now? And where are you headed? That’s one part. And second, please tell us a little more about your health system. 

JD: Yeah, sure thing. You mentioned Navy and Air Force—yes, I did start out in the Navy. I wasn’t doing healthcare there; I was doing Navy things, driving ships around.

Then I got a master’s in healthcare administration and started healthcare work in the Air Force. The job I had there was mostly in healthcare IT management.

So really, by the end of my Air Force career, I was doing very similar things to what I do today. And a little more on Dayton Children’s—we’re on Epic. We’re big enough to be on Epic and Workday, which I think probably factors into some of the things we’re going to talk about. 

We’re small compared to most health systems. So what does that look like?

It means we have to do a lot of the same things that bigger health systems do, but it can be challenging to have the resources—people and dollars—to get all those things done.

Of course, when you bring your sick or injured child to Dayton Children’s, you have the same expectations for quality and experience of care that you’d have at a larger children’s hospital—like Cincinnati Children’s or Nationwide Children’s in Columbus.

So yes, the challenge is keeping up with larger health systems, but with fewer resources.

Q: I see, I see. And an increasingly difficult environment lies ahead. So I’m sure there are more challenges on the way, and I’m sure the leadership is already thinking about how to navigate those challenges—especially, and we’ll get to that—no podcast is complete without AI. We’ll talk about that in just a moment.

But before that, what I would like to ask you is—you mentioned that you are on Epic and Workday. So please tell us a little bit more about how that drives your innovation, or let’s say, the consumerism from the digital front door perspective. Any initiatives like that?

JD: Sure thing. So in both cases, we spend a lot of money for the care and feeding of those platforms—both in dollars to the vendor and in terms of all the labor that we need to put into them. That’s the bad news.

The good news is we have best-in-class platforms in both cases, and we can do a lot of innovation just by optimizing within these platforms, including some of the generative AI features that both vendors are doing a very nice job implementing into their platforms. That’s very exciting.

We’re early-ish stage with some of that, but the point is—it’s a lot easier to implement these features from within the platform than try to bolt on new things. In some cases, we’ll be bolting on new things, like ambient, and maybe some autonomous coding and some other things.

I’m not saying we won’t do that at all, but probably 90% of what we would do with generative AI would just be from Epic—or I should probably also throw Microsoft in the mix. We’re a Microsoft shop, so we’ll be using some Microsoft tools also.

 Q: Yeah. So could you talk to us, JD, about some of the generative AI use cases that you perhaps are already looking at or might be on the roadmap of these vendor partners that you are going to be adopting? 

JD: Sure. Well, one obvious one is ambient. Most health systems—if not fully in production—are at least piloting or about to pilot something with ambient. I think very soon here, having some ambient solution will be an expectation from providers. And health systems may have difficulty recruiting new providers. And of course, as we have more challenges with physician shortages, that’s going to be a challenge.

One dynamic at Dayton Children’s is, of course, we need to successfully hire pediatric specialists. And they typically are getting out of their pediatric specialty fellowships at large academic medical centers. To convince them why they should move to Dayton, Ohio, we can’t be at a competitive disadvantage to some of the larger facilities. If we are, for example, not using ambient, we’d like to be fast followers. We’re not going to be the first to do things. We’ll leave that to the academic medical centers and some of the truly new things that they’re developing—both on the clinical side and the digital health side.

One of the nice things about being an Epic customer, of course, is there’s such wonderful collaboration between the whole Epic community. If you do something innovative in your Epic build, you go to the Epic conference, you present it, and other people can use that. That’s sometimes what Epic will just build into the next version of Epic. So an awful lot of that goes on all the time. And Epic is rolling out so many new features so fast, it’s actually difficult just to keep up with all the new features that are coming from Epic.

Q: That’s true. It’s a large system, JD. So how do you separate the wheat from the chaff? That’s something we were kind of hitting on before we started the podcast. What are your thoughts on that? How do you decide what is critical and core, and what can be done later or perhaps doesn’t need attention right now? 

JD: Sure. So as a general concept—just good governance, right? And not chasing after, as we like to call them, the “bright, shiny objects.” Even with core generative AI, you’ve always had that problem. Somebody goes to a conference, they see something that looks cool—and it may be cool—but there’s not enough return on investment to spend the dollars we don’t have on that thing.

So we’ve had that challenge for a long time. I would say generative AI has ramped that problem up a few notches because there’s so much hype. You have to be careful—not just about wasting money, but also the additional considerations that come with generative AI that we didn’t always have with other things. Things like ethical considerations and medical-legal concerns. So we need to pay a lot of attention to that.

I try to stay up on all this, of course. And when I listen to very smart people who spend their entire lives focused on generative AI, they often talk about the investment bubble. Two things can be true at the same time: One, there’s amazing science and capabilities advancing very quickly. And two, a lot of the investment money pouring into this is going to be bad investments because nobody’s going to pay a gajillion dollars for that thing you built.

So that’s where you have to be very careful. Now, how do we handle that? Well, we handle it like we always have—by asking hard questions about ROI. And sometimes we do things that have more soft ROI than hard ROI.

Ambient is a great example. Reasonable people can disagree about the hard ROI, but there’s really no question about the soft ROI—keeping our providers happy. You hear story after story: “I was about to retire early,” “I was burned out,” and “this really brought back the joy of practicing medicine.” Pretty much every system that’s implementing ambient gets dramatic stories like this from providers.

Soft ROI is important too. You just can’t buy everything that has soft ROI—you have to be judicious.

Q: We had touched upon using some of the new tools that are coming out for enabling coding. What are your thoughts on some of these tools, JD?

JD: Yes. This is an interesting space. It may be something we work on with additional vendors. In fact, we’re about to go live next week with Epic’s professional billing—what I believe is called the DB Coding Assistance. It’s a lighter-weight AI solution aimed at making our PB billers’ and coders’ lives a little easier with some tools from Epic.

There’s a spectrum of billing complexity—from professional billing to hospital outpatient and inpatient. From what I understand, inpatient is still too complex for full autonomous coding. But in the hospital outpatient space—that middle ground—autonomous coding, thoughtfully applied, can really help our coders and billers be more efficient. We’re exploring some of those vendors to see if there’s a good fit for us.

Q: That’s great. So, as a smaller health system, how do you approach innovation? How do you keep up with the larger systems and still deliver quality care? 

JD: Sure. Something else—there’s a term commonly used in the Epic ecosystem: “imitate to innovate,” right? If you can get past the concept of not being proud about implementing something that somebody else developed someplace else—that’s really the answer. We like to say we want to be fast followers. Most people in IT are familiar with Gartner’s hype cycle—the peak of inflated expectations, the trough of disillusionment, and the plateau of productivity. We’ll let others go through the trough of disillusionment. We want to be there for the things that actually work.

We’re not just rolling the dice on whether something will work. No, that worked. This thing worked at another children’s hospital. And we know those people—we have really good relationships with pretty much all the CIOs and CMIOs at the other children’s hospitals. We go to conferences and talk to each other—“Oh, that new generative AI feature from Epic worked wonderfully for us,” or “that one didn’t work so well—it wasn’t a good match for pediatrics,” or whatever the case may be. We talk to each other and increase our confidence. Nothing’s ever 100%, but we’re more confident that it’s worth the effort.

Q: Right. And JD, you’ve been at health systems that weren’t pediatric-focused as well, right? So what’s the difference? I’m curious—in the world of pediatric hospitals, how are things different compared to other health systems?

JD: Sure, thanks. Some things are different with pediatrics. It’s unfortunate, but it also just makes sense—the way the world works. When you have innovators and venture capital funding innovation, a lot of the dollars go to adult medicine because that’s where more of the money is. Pediatrics sometimes plays second fiddle.

Maybe Epic rolls out a new predictive algorithm that works better for adults than for pediatrics. That was true for the sepsis predictor. I remember a pediatric CMIO talking to me about why that was. So we just have to be cautious.

Other examples—imaging. Now we’re talking more predictive than generative AI. Some of these are technically generative, but there’s a lot of FDA-approved, highly effective new imaging tech powered by AI. I was talking to our radiologist about that, and at their conferences, they’ve noticed there hasn’t been much for pediatrics yet. A couple things—bone age prediction, maybe one other—but that’s about it.

So sometimes we just have to wait. In other cases, there are people doing innovative things targeted at pediatrics. We’ve been looking at a couple of NICU-focused solutions—for a better parent experience. Your precious little new baby, sometimes very tiny, is in the NICU. You have to learn a lot quickly—talk to the doctors and nurses and figure out what that all looks like. How can we make that experience better?

Also, we’re an ACO—we want to make sure we’re spending our dollars wisely. We want kids in the hospital when they need to be, and home when they can be. Some solutions around tube feeding, oxygen—where we can send infants home earlier than we otherwise could with better remote monitoring and communication tools. In some cases, there’s real innovation going on that’s very specific to pediatrics.

Q: That’s great to know. You also mentioned Workday, along with Epic, as a major system. Have you seen anything on Workday’s roadmap that you’re considering? 

JD: Yes, we were just looking at this yesterday in our Workday executive governance meeting. We asked our account team to put together a chart of all the generative AI features Workday has. There were a lot—it all had to fit on one slide with pretty small fonts. We color-coded them—what we’re licensed for and using, what we’re licensed for but not using yet, and what we could be doing but aren’t licensed for.

One big difference between Epic and Workday when it comes to AI: Epic has a deep partnership with Microsoft. That’s where the generative AI and cloud compute happens—in Azure. Epic is hosted on-prem for us. A lot of Epic customers are still on-prem. Workday, by contrast, is built with modern cloud architecture from the ground up. They don’t need to partner with anyone—it’s just built into the platform. Both vendors are doing AI differently based on their system architecture.

Q: So again, JD—just a curious question because I’m trying to build a picture in my mind. Let’s say Epic and Workday are two major systems that are clearly top of mind. Are there two or three other systems, not in the same space, but in different domains, that are also driving your AI or GenAI roadmap?

JD: Sure. For health systems, another very strategic area is your PACS vendor. For modern PACS vendors, you want them to plug into all the AI tools that are coming. In some cases, they can do that natively. In others, there’s middleware that adds AI features. You want to be able to add that capability easily.

Then there’s the vendor-neutral archive—getting all the images in one place. We haven’t always done a great job with that. If a radiologist ordered and read the image, it went into PACS, but other images—ordered and read by other providers—sometimes get squirreled away. That’s not ideal, especially if you want to apply AI across images. A vendor-neutral archive is typically a better architectural solution.

One of the most strategic acquisitions we’ve made in the last seven years was PACS. When I got here, we were already on Epic, and Workday was just starting. But PACS—we were replacing an old legacy system. I knew we needed a PACS and VNA combo that would be future-proof. We selected Sectra, which has long been considered best in class, and we’ve been happy with them.

There aren’t a lot of pediatric-specific AI tools yet, but we have good confidence in our direction. We’re doing well getting the other images—like point-of-care ultrasound—into the VNA. So as AI tools become available, we’ll be able to implement them easily.

Q: You mentioned the vendor-neutral archive, and that reminded me: for AI, we need data, right? And we need data engineering on top of that. But data’s often siloed—Epic has its data, Workday has its own, PACS has its own. How do you approach enterprise-wide AI that cuts across systems or functions? 

JD: Good question. If we had more money and more time—and maybe if we were a big academic medical center—we’d be spending more on a true enterprise data warehouse. Honestly, we don’t really need that at Dayton Children’s. The vast majority of our reporting and analytics lives in Epic.

That said, we are dabbling with Microsoft Fabric, because that’s where Epic is headed for the cloud. We’ve been doing Power BI for a while. For our enterprise scorecards, we bring in Epic data, Workday data, and other sources to build dashboards for leadership.

Another example—patient survey data. We take survey data from our vendor, mash it together with Epic data, and build dashboards. That helps us drill down on metrics like Net Promoter Score, which we’re proud of. And we’ve done analytics around that—asking why certain patients in the ED at certain times aren’t satisfied. You can’t tell just by looking at the survey data—you need the Epic data too. Then you can better implement fixes.

Q: That’s very thoughtful and insightful. JD, I know you mentioned that you also do some consulting. Can you tell us more about the kinds of engagements you take on and what excites you? 

JD: Yeah, I do a little consulting on the side, after hours. I joke with my boss that it makes me a better CIO—it helps me stay connected. I talk to people—sometimes investment companies—who are trying to decide whether to invest in a particular software solution. They want to talk to someone actually using it.

Sometimes people have a new product idea and want feedback from someone like me. I see things coming into the market—vendors acquiring tools and building platforms. Most of the work I do is one-off expertise. I’ve been doing this for a long time.

Occasionally, I help tech companies on an ongoing basis—helping with go-to-market strategy and how to sell into health systems. That kind of thing.

Q: Thank you, JD. As we wrap up, are there any other thoughts or things you see coming in the future that you’d like to share? 

JD: One last thought—it always comes down to good governance. I talk to other CIOs and CMIOs who struggle with too many requests. “How do we deal with all these requests?”

Something that’s important—but not fun to talk about—is acquisition policies and governance. People are getting wrapped up in all the new GenAI stuff. You need to think hard about the ethical and medical considerations and build those into your evaluation and procurement processes.

What I see a lot of people doing is starting 17 new AI committees. And I think, who has time for that? It’s better to work AI into your existing governance structures and procurement policies. But those policies have to have teeth. You can’t let everyone buy any IT thing and toss it over to IT to implement.

Then IT ends up unable to do the core things we’re supposed to be doing because we’re trying to plug in new tools that don’t fit the architecture.

Q: Understand. Thank you, JD. This has been a great conversation—really appreciate it.

JD: Thanks for having me. Hope we can continue the conversation in the future.

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Disclaimer: This Q&A has been derived from the podcast transcript and has been edited for readability and clarity.