← Becker’s Healthcare Podcast
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- [00:00]
A
Hi everyone, this is Lucas Voss with Becker's Healthcare. Thanks so much for tuning in to the Becker's Healthcare podcast series. It's great to have you. I'm excited today to be joined by Charlie Lloyd and Dr. James Whitfill for our episode on how robust provider data and AI are becoming a key part of the infrastructure for patient access. Charlie is the CEO and co founder of actual. He's redefining the foundational infrastructure of the healthcare workforce. Under his leadership, Actual has moved beyond simple verification to become a strategic data partner for the nation's leading health systems. By leveraging real time provided data, Charlie and his team are transforming how organizations source, recruit and deploy clinicians. They're focused on solving the fragmentation of the past to build a modern data driven ecosystem that accelerates onboarding and drives the long term clinical and operational transformation required in today's market. Charlie, welcome.
- [00:55]
B
Thank you Lucas. Good to be here.
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A
And we also have Dr. Whitfield on he's the Senior Vice President of Strategic Partnerships and Chief Transformation Officer at Honor Health. He brings a unique blend of both clinical expertise and executive leadership with a strong focus on driving innovation, scaling strategic partnerships and advancing transformation across complex health systems. His work centers on aligning technology operations and clinical strategy to improve outcomes and create a more sustainable model for care. Dr. Whitfield also so great to have you. Thanks for being here Lucas.
- [01:27]
C
Awesome to be here. Really glad.
- [01:29]
A
I want to hop right into our conversation. We certainly have lots to discuss. And Dr. Whitfield, I'll start off with you. And this is a little bit of an awareness piece here too. On the first question. Most people think that provided data governance is basically just keeping a list of doctors up to date. It should be pretty simple. We have a list, we update it regularly. Done. Can you explain why that's off and what happens if an organization views it just like a list that needs to be updated?
- [01:58]
C
It it's first of all, Lucas is a great place to start because it does it seems like it should be pretty simple. Not unlike the old saying baseball is a simple game. You just throw the ball, you catch the ball, you hit the ball. But it when it comes to our physician advanced practice provider data that is the lifeblood of it's the information that drives so many different parts of the health system. And I think what people don't realize is that there are lots of different systems keeping those lists but they need to keep them for different reasons and they different views of what the lists mean. So for example, an example that I always like to give is that. Imagine that if I have a physician that does obstetrics and gynecology, right? That's a type of medicine treating women delivering babies. That person may be licensed in a specific way and credentialed, say from a board certification. That's a piece of data that's really important because if you have rules that that person can't practice in your environment without that certification, and you need to know that. However, when a patient is looking for somebody to care for them, they want to know, does that, does that doctor deliver babies? That's a different question. Obstetrics is still in the word, but actually now you're talking about a different piece of data that may be held in a different system. And this repeats itself in the electronic health care record in the system, the contact management system, in the phone system, in all of these directories. And then, oh, by the way, you multiply that by 5,000 or 10,000 physicians and that information is changing, sometimes daily for different individuals. Who's in charge, who's managing it, what's the authority? It can get out of hand really quickly.
- [03:43]
A
Yeah, there's so many data points that need to be cross checked, et cetera, which is so crucial. And again, it creates mistakes, as you just outlined. Now, Charlie, we're bringing AI into the mix right now. And I know in a previous conversation we've briefly touched on AI hallucinations and they still exist, they're still there. But again, you're also talking about something that's already happening in scheduling and referral tools before AI even enters the room. Really? What does all of this look like for a real patient?
- [04:13]
B
Yeah, Lukas, I think I like to sort of start at the basis of data. And if you look at provider data, it is that directory. And part of the issue today is that provider directory inaccuracy is really creating a problem in the us not just from the payer side, but on the health system and provider side as well. And the culmination of that in many ways is you've got inaccurate and outdated provider data that's sort of making the supply and demand problem almost worse than it already is. And so, case in point, matching a patient to a physician requires a lot of up to date information. It's really important to have that right. It includes things like when and where that provider actually practices, what the patient profile of that provider is, and what conditions they treat of those patients. There's also specialty and specialization. Sometimes that gets missed. And to Dr. Whitfield's point here earlier, you've Got in some cases, you sort of need to look at that from two different angles. There's the medical terminology and then there's the consumer terminology. But nonetheless, if you think about specialty and specialization, specialty might be cardiothoracic surgery, but specialization means that they do mitral valve replacements versus CABG or orthopedic specialty, maybe that they do a specialization may be that they do hands versus feet or whatever that might be. And those nuances are really important. Other information that's really critical when you're making decisions about matching a patient and a provider together are things like, are they, what is their availability look like relative to the utilization, not just the schedule, what's their skill level? That's going to be really important in the referral process. But when this isn't applied or becomes stale, you've got situations where there's mismatched appointments, there's referral opportunities that are lost or missed or poorly optimized. There's just overall waste. And so as we think about this, the root of the problem is that most directory data is created by people rather than derived by real world data. With human oversight and, and that status quo in provider data upkeep is really something that's a big challenge across the space today. Director Data that's entered by providers or worse yet by people once or twice removed from the provider is going to be inherently biased, it's going to miss details, it's going to be inadequate in many cases for a really good consumer search, let alone AI. And so without that automation in real world data, administrative staffs have a tough time keeping up. But if we think about AI, if you put AI on top of bad data, it's going to come up with some bad suggestions. So if we don't want AI to hallucinate, step one is don't feed it the data equivalent to peyote. And so we just sort of look at it that way. And like, you've got to get that data right in the very beginning.
- [07:18]
A
Yeah. Now Dr. Whitfield, we touched on this a little bit because again, when we're getting this data, right, we talk about data governance, et cetera, but it also touches so many different elements of an organization, right? We talk revenue, psycho, medical staff, emr, even marketing. In a lot of cases, that needs data to be able to do this, right? Who owns all of this inside a health system? Who should own it? What's that ownership level? And again, I think the more important question here too, what goes wrong? When nobody can answer that, when nobody can tell, okay, I don't know. Who owns this.
- [07:51]
C
Well, first of all, it is a total recipe for disaster when you ended up in that situation where nobody. And by the way, it's easy to fall into that situation because you've got all these different stakeholders that have all these different perspectives. But for the most part, they're busy, they're worried about their stakeholder stump. They don't, they can't worry about the whole picture. In our, I'll tell you our journey in our organization, we ended up saying that we think that the data quality around our physicians and advanced practice providers is so important that we put the, that was the first element that we brought into our analytics team. And the head of our analytics department said, all right, I'm going to chair the provider data governance group because there is no one person who owns all of this work. And when we think about the most important data elements for an organization, there's something called data stewardship. Like who, who owns this piece of data, who's responsible for monitoring its quality and who convenes, you know, the work to make sure that it's done correct. We knew that we needed to do that. This was the first stake in the ground. We said, provide the physician data or provider data. That's where we're going to start.
- [08:57]
A
Charlie, I'm assuming you have seen this go wrong. Same question for you, right? Who should own this inside a health system? Who owns it? And again, what goes wrong if that's not the case?
- [09:09]
B
Yeah, it's a good question. I mean, it's almost like a hot potato to some degree for a lot of organizations because it's a lot to take it on. And it's been this wide surface area problem, meaning like so many, so many cooks in the kitchen, so many people have been trying to address it. And you know, to, again, to Dr. Whitfield's point earlier, depending on the use of that data, you've got different people updating it, manipulating it, adding to it, et cetera. And again, I think with all those people touching the data, you've got just a lot of administrative costs. You've got duplication and that duplication. We've seen this in many cases, it's really not accounted for. Yeah, it's sort of built into the budgets in different departments. But, and they're also, they've got either a, in some cases one or two full time people working on it, or maybe it's a half a person or maybe they're subbing it out and they've got other folks trying to Gather the data from many different sources, but there's just a lot of redundancy and cost in that. That obviously isn't a good thing for health care right now. Right now. But moreover, you know, I've also seen, if you think about where it goes wrong is you've got so much investment. You know, you look at other industries outside of health care, and when they add technology, efficiency improves and costs go down. We haven't been able to do that to the extent that health care deserves it. It's just in general. Right. And I think that, you know, we look at that and say, like, how can we begin to fix that? And, you know, you've got situations where you've got a very robust EHR infrastructure that, that organizations in some cases have spent tens of millions or hundreds of millions of dollars to put in place, but they don't have the data to power all of those capabilities. It can be really limiting in terms of the return investment that we're all looking for. When you look at a lot of our customers are epic, right? And it's like to be an epic cosmos, you have to bring good data to use some of these tools like teamwork and cheers and CRM and other things. If you really want to get the good value out of that, you've got to bring your data a game. And a lot of this has to do with just figuring out where in the organization needs to sit. In my opinion. I think a lot of this really needs to be in the wheelhouse of the CMIO or CIO or in some cases, Chief Data Officer. Yes, the CMO absolutely cares about, the CFO cares about it. But those are folks that really understand the data. And so I think we're beginning to see some of that coalesce around those types of teams because they've worked with it in the past and they understand the implications from an intersystem standpoint of getting this right.
- [11:53]
A
You've both now touched multiple times on the importance of getting the data right in any instance. And again, we know that clinician data is really the fuel for a lot of this. I want to turn this around a little bit and talk about something that's happening across healthcare, which is the race for agentic AI, the agentic layer in healthcare, so to speak. Again, if that clinician data is the fuel, what happens to those AI agents when that underlying data is wrong, outdated, or just incomplete that we've just talked about here in this conversation? Charlie, I'll start off with you.
- [12:34]
B
Yeah, Lucas, you know, I think again Same things if it does, if the underlying data isn't there. Having an agentic conversation or conversational AI around finding the right physician, matching a patient phenotype with a provider phenotype. If you don't have the right information, you can't expect it to do much other than maybe get it wrong. And so I think that is obviously really important. But I also want to look at what the potential and the value exists there to be able to not only provide a consumer interface that is conversational, that a lot of these agentic technologies are providing, but also learning from those conversations what patients need as consumers, as caregivers, as advocates, because that's a lot of where that's happening. You know, that that is data that's really important to capture along the, along the way as well. I think, you know, agentic conversations are going to give us the ability to take these fairly. You know, a lot of the organizations work with, have invested in a, a ton of money and effort into, into models that say if, if this X, Y and Z exists, route to this type of clinician or that other type of clinician, all that stuff is really important. But being able to layer AI on top of that and see how it operates, I think is something that's going to be really interesting. And to some degree, it's how consumers are beginning to think about how they interact and have questions today. I mean, a lot of this we can just learn from how search in, how they're searching and how they're behaving. So you kind of need to be able to bring these two roles together. And I think having, again, a good data substrate to do that is going to be really important. Yeah.
- [14:20]
A
Dr. Whitfell, same question for you. When we talk about agentic AI, how are you viewing that and again, specifically related to the data in and of itself? How important is that when we're thinking about that AI agent concept?
- [14:31]
C
Well, I'll use a real world example. We are now deploying an agent that helps with patients who are coming in for a procedure, say they're going to have an operation in a week. There's a lot that you have to do in order to be ready for that. And the last thing we want to have happen is that somebody shows up on the day of their procedure and we miss something. Right. We have to cancel. That's terrible for the patient. It's not good for the health system. So one of the things that sounds very pedestrian, but the difficulty of getting medical records from lots of different places oftentimes involves phone Calls to physician offices to ask for medical records that haven't been set yet. I mean, it's terrible that we live in a world like that, but the reality is, since we have an agent that is starting to do that work, just that's a back office function. It's got to know the right physician with the right back office staff, with the right phone numbers and the right context of how to get that work. And because we can start to do it at scale, and I think that's the part around the agentic pieces. If you don't get it right, you can do a lot of, at worst, at best, like embarrassing, kind of embarrass yourself at scale really, really quickly. And so as we think about those agents that are starting to reach out to other human beings, other offices, eventually probably to other agents, like making sure that they're going to the right place at the right time for the right information, and getting that all straight is really critical for that agent to do its workflow.
- [15:54]
A
Yeah. And Dr. Whitfield, as we close out our conversation here, again, I want to bring this back to the patient because the patient is at the center of all of this. It should enable patient care, better patient outcomes. At the end of the day, that's the crucial piece to this. Right. And we know that again, especially in areas where there are, you know, maybe physician shortages or there's not as much access. Right. Patients tend to ask for a specific doctor, that person might not be available, and then again, to your point, they won't come back. It has an impact on the organization beyond just the clinical piece of it. It's a brand, it's reputation, it's financial. How does smarter data governance help a system then offer a real alternative? Right. Without that patient feeling like, okay, I just got handed off again to somebody else that I don't know, that was just happened to be free.
- [16:43]
C
Well, Lucas, I still see patients, and I'm haunted by the following scenario, which happens too often. Let's say it's a long way to get a hold of a specific kind of neurologist, because I've got a patient I'm really worried about. And if that patient has to wait 30 days to get to that appointment, and then they show up at the neurologist and it's the like, it turns out that that neurologist doesn't treat that condition. It's not. I. I've just wasted 30 days of that patient's life. Right. And now it's to start that process all over again. I feel terrible for the patient. It's embarrassing for us as a health system. It's bad for our brand. And by the way, I wasted the time of that other neurologist who now can't see somebody else. Right? That is, that happens. And that's because we don't have the right information around our physicians so that we can make that right choice. You asked a broad piece, but I will just shrink it down to that very tangible sort of patient story. And that's what drives us as an organization, to make sure that our physician data, our advanced practice provider data, is kept as accurate as possible so we can really drive more efficiency. But get it right. Because if we don't get it right, it doesn't matter how efficient we are.
- [17:51]
A
Charlie, really quickly, before we close it, just to bring it back to what we started our conversation out with, should this be a priority for every health system across the country in terms of looking, not just looking at these things as lists and looking at data governance as a priority that needs to be looked at?
- [18:08]
C
Yeah.
- [18:09]
B
Lucas, I would end by saying this is really about maximizing access. We have a supply and demand gap in the United States relative to care, and it's just going to get harder and harder for a lot of different reasons. And if we can use technology and good data to make better matching decisions, like Dr. Whitfield just said, that goes a long way to stretching without adding burden to clinicians. That goes a long ways to improving some capacity and improving access. And it's a national imperative. We've got to figure this stuff out. Trust is incredibly important and, and we have to be thoughtful and somewhat conservative about how we outreach and interact with patients. But it's just something that I think we all need to do. And it's great to see forward thinking organizations like Honor be behind this kind of thing.
- [18:59]
A
Well, Dr. Whitfield, Charlie, so great to have you both on. Thank you so much for being here for your time and insights. And we also want to thank our podcast sponsor, Actual, for bringing us together for this important conversation. As you just heard, a national imperative. And you can tune in for more podcasts from Baker's Healthcare by visiting our podcast page@beckershospitalreview.com.