Episode 124: How is artificial intelligence revolutionizing the healthcare sector?

Coffee with Coker is a healthcare business podcast from the Coker Group that focuses on solutions to help healthcare organizations effectively navigate the changing healthcare industry landscape. Well, welcome back to another episode of Coffee with Coker. And today we have a very interesting discussion and one of my colleagues here at Coker is joining us today. Just for those that might be the first time they're listening.

My name is Mark Reiboldt. I'm an executive vice president at Coker. And really what we want to do here is talk about various matters related to the business side of healthcare. We're specifically talking about AI or artificial intelligence, which is something I feel like everybody has heard of, heard about more and more, especially within the last couple of years.

But the role that it's playing in healthcare that we're already seeing today is really increasing. And so we thought it was, we've talked about it a little bit before we've published material on it, but I feel like this is a really good opportunity for us to dive into a little bit more detail. So I'm happy to have with me today, Vinson Do. Uh, as one of the Coker team members and, and Vinson's really taken point on putting a lot of research to what, how AI is playing more of a impactful role within healthcare delivery, both on the business side and the clinical side.

Today we're really talking about what I would call more on the business side. Obviously we're not getting into clinical discussion, but there are a lot of AI applications being used on the clinical side, which is another interesting piece. Vinson, welcome. Thanks for joining.

Thanks for having me, Mark. Appreciate it. Yeah. Thanks for the introduction as well.

Absolutely. Glad to have you on. By the way, before we jump in, we'll have some supporting materials and the notes to the episode, and you can always find more information on the Coker website at Cokergroup. com.

You can find more information about Vinson. So I really encourage people, if you're interested in this topic or any of the things we write about, go over there to Cokergroup. com and you can find all of our thought leadership and published material. Vinson, why don't we jump in and I feel like a good place to start here is just what is AI and just a brief overview generally for those that may not be as versed in it.

It is a great starting point to have and I think that for our viewers out there that aren't familiar with AI and I know it's a buzzword but let's start with just the word intelligence because I think that's important. Merriam Webster's dictionary would define it as the ability to learn or understand or deal with new or trying situations. But I think I would simply put it as the ability to just accomplish complex goals. So, according to Garner's multiple theories of intelligences, we have eight.

Of them, and over the last 160, 000 years, we've just evolved these eight types of intelligences, which include visual, linguistic, logical, um, body, musical, interpersonal, intrapersonal, and naturalistic. These are the eight that we've evolved over time, and that, I believe, has now evolved into something that we have as a different category known as artificial intelligence. And so, I think that it mirrors our own intelligence, but it can be more. We just don't know yet.

This is a very uncharted territory and there's a lot of seed and saplings that are happening, but it's eventually going to grow. And I think as now, when we define what artificial intelligence is, Merriam's Webster would define it as ability for computer systems and algorithms to imitate human intelligence. But actually, I think Sean Cass from SAP put it simply as intelligence exhibited by non biological systems. I think that takes away the meaning of mirroring our intelligence and it's forming its own intelligence.

So that can be a, a, a way to just describe that. It's just non biological systems exhibiting intelligence. Yeah, no, I know there's a lot of general discussion out there as far as machines becoming smarter and smarter, whether it's the chat GPT type of stuff, but really it's something that's been evolving now for some time ever since really the beginning of the technology itself. And I know there are a number of layers that encapsulates AI and we're not going to go into all of them today, but sometimes I think different terms are used interchangeably, but what I really want to jump into for our audience is, okay, how does this play into health care and health care delivery and the health care industry?

Because again, with our work that we've done in technology. And even beyond just the technology side of Coker, but really in all of our services, there are numerous applications of AI being used, whether it's on the financial side, whether it's on the actual technology side. And as I mentioned in the intro, even on the clinical side, but. Talk through how we're seeing AI in different segments of healthcare.

I think AI has really over the last 20 years, it's been transformational and it's such in the span of time it's took enough like a rocket ship and especially when we're At Coker, we deal with the healthcare space. So the three segments that for today's discussion that we'll be focusing on is claims processing, medical coding, and healthcare reimbursement. These are typically the three primary segments we're seeing at this time recording in 2024 of where they're affecting in the healthcare space the most.

Yeah. So yeah, that's very helpful, very relevant because claims processing, medical coding, and reimbursement are all things that In terms of Coker and our services, we're very actively involved in with our clients and navigating different things in those realms. So let's just start, let's go through them. What about, what are we seeing in terms of AI and claims processing?

How, and like, how does that play out? I think What I've brought to this discussion is three use cases of where we're finding these examples of AI applications working, um, in tangent with the claims processing system. And for our viewers out there, claims processing, it's very important, but it's just a subset of the health revenue cycle. And we're seeing case number one is in community medical centers of Fresno.

Um, Eric. Eckhard, which is the director of financial services there. He was observing that the payers have increased these delay tactics, which has caused a huge burden on their administrative team. So a lot of these claims are being sent back.

And what they decided to do was in 2015, they implemented a new AI based system that could predict potential denials before the submission. What they saw and what they observe is they could decrease the denials by 18 or 22%. These numbers also indicate that because of that, they're saving a lot of this time and time is obviously crucial in terms of getting back accounts receivable and receiving payments on those claims. We're seeing that's really helpful is that they're able to guide the staff to follow up on these cases that have higher chances of successful appeals, meaning that no longer does a person have to sit there and prioritize it themselves manually, which one is the more important claim.

The AI system helps and pushes those, the top that have a higher chance. And obviously you're going to get a higher reimbursement on those claims and successful claim processing. Yeah. Which not only has a administrative burden and the timing of it could be very challenging and complex, but the financial impact, right.

That's that plays into everything in terms of revenue cycle management. So that in terms of optimizing. Revenue cycle management, that's a huge impact. They're decreasing denials by up to 22%.

And you said he did that. They implemented that in 2015. Is that right? I, I believe so.

I, yeah. Yeah. I believe they had implemented for a long time, but obviously this takes a lot of time of development. So the development time before that could have been a lot much more further than that.

And Mark, if you don't mind me adding on is that there's other cases as well, right? We're seeing that we're a Hattiesburg clinic in Mississippi, not advertising for Experian products, but they're using an Experian products such as claim source. And so what claim source is, is a automated claims management system that allows to check, um, the errors and prioritize those claims. So similar to what we're seeing.

In the community medical centers of Fresno, we're seeing that same tactic being used in and same device being used for Hattiesburg clinic. So they claim that they've reduced denials by 6. 1%. And just to let you know, the industry average is around 10 percent of claims are denied.

And so they've reduced it by 6. 1. So they're claiming at 4 percent that only 4 percent of their claims are being denied. It's very impressive.

I'm not sure if a human is able to do that, but that's just some of the ways that we're seeing AI transform this whole industry. If a human was a, it would probably take a lot of them and it'd take a lot of time. And yeah, to get that kind of impact in that amount of time. You're right.

That's a very impactful change for that systems reimbursement. I just wanted to bring up one last case for those viewers who are questioning if, is it really creating impact? We have another one, which is Summit Medical Group in Oregon. Um, they're also using Experian with their enhanced claim status tool and their claim scrubber, reducing accounts receivable days by 15 percent and a 92 percent first time pass through rate, which is insane, right?

That means that 92 percent of the time these claims are going directly through. They're no longer being held back or they have to be formed for resubmission. So these are just some of the three cases that we're seeing out there in the world that AI has created an impact on this one organization. Imagine if we're taking that to a scalable amount where a bunch of people are adopting AI and I think that's going to create a very drastic landscape.

Yeah, very compelling cases there, and I agree, I imagine there are probably a lot of, a lot more cases out there, particularly now in 2024, and I think more and more solutions are going to be coming online that allow them to do that, and I think one of the things we talked about A human being able to do this or how many humans it would take and how long the time piece is very important when you're talking about rev cycle that goes directly into cash flow and reducing the, um, AR days is another big financial benefit for something like this.

I think a lot of people are worried all around any industry that AI is going to take away jobs and computers doing things that humans used to do. I think when we're talking about claims processing and a lot of these AI applications in healthcare, We're not really talking about doing something instead of a human. We're talking about things that improve on what humans are already doing and staff is already needed, but we can improve on that with the technology. Would you agree?

Yes, I would totally agree with you, Mark. I think for those sci fi fiction fans, you've grown up watching all these videos and then movies like iRobot of things that are taking over human life and this and that, but it's not as serious as such. This is almost like the boom of the internet. You can, now you can't imagine a day without your phone and the internet.

So imagine it's not taking over the job. It's sorting through those massive amounts of data, things that it would take me, you Mark, and maybe this entire firm 50 years to go through, but it's doing it within a split second because of processing power and it reprioritize it. It flags it and it allows the human input. To be in there.

So I think it's just acting as a helper to the human ability, right? So always, it's great for people to kind of speculate it might take over, but it's also good to see the other side of it, which is that it's incredibly helpful. I think that's a benefit for the organization too. It means.

They're not having to add a bunch of people, but they're able to get a really positive impact with their existing resources, just leveraging the technology. And I think that's a point that I really want to impress upon folks is. We're not talking about computers doing something instead of humans, we're talking about augmenting it and enhancing it, which kind of ties to the second category that you mentioned, which is medical coding. And Coker has for many years been heavily involved with our clients and, and coding and.

We don't do outsource coding. What we do is audits and compliance work around coding. So maybe speak to some of the, the cases and examples of how AI is being used in the coding realm. Cause we've actually, we've used some AI in terms of being able to process more and better audits.

With different technological tools, so speak to that, if you will, of course, I think now the 2nd segment of healthcare space is medical coding and medical coding is a very complex place to be. So 1 example, and 1, 1 use case, it would be in Mass general Brigham in Massachusetts. They face this problem of insufficient and inaccurate medical records. These.

Codes evolving over time. And then the compliance and audits, like you mentioned, Mark, are just some of the many issues that they also face as well. And. Why our clients reach out to us for help, but in 2015, and I want to put an asterisk that I said 2015 for the last use case, I meant 2015 for mass general was this use cases example when they implemented this AI based system, which is now called code metrics.

What it does is that essentially it predicts. And whittles down the plethora of CPT codes down to just a handful per physician to be able to accurately predict what CPT codes that physician will use for this encounter. So how does it work? The algorithm essentially looks up the provider notes, provides a long ranging support of evidence, and then they have a service that if the provider has done these CPT codes often over time, that can be completely automated.

Obviously with the human input of the provider to approve of it, But this is how at the present fee for service where we're using just it has to meet the medical necessity. This is where AI comes in and takes over these niche fields that will save time for physicians. And so that's how it's ultimately affecting medical coding. Yeah, that's interesting because For those anyone that's somewhat familiar with coding is a there's now well over 15, 000 codes and that's just mainly on the inside of things that didn't get into a lot of the surgical coding and other types of coding and they're changing evolving every year.

So the government, the CMS will send down changes updates to coding annually, and that changes the fee schedule for those codes. And so just navigating that data has always been challenging. But then you get to, again, the compliance side of that and ensuring you are. Properly coding, properly coding in many different ways, meaning not just the right codes, but the right levels, the code levels and things like that.

And again, you, it just gets more and more technical as you get into more technical specialties or depending on the specialty. You know, that's something again, Coker works with our clients, many clients all over the country when it comes to coding audits and making sure there's adheres to compliance standards. When it comes to coding and I can see how this could make things simpler for say, the provider just in terms of implementing codes. But when you're talking about even downstream, how this could help really enhance compliance, you're talking about a lot of savings and loss prevention, preventing hassle.

I mean, if you're found out of compliant, there's so much that goes with that. There can be financial. Consequences, there's administrative burden, there's time, many different ways that I think we have to consider how this could help in the coding side. And that's just the domino effect of it, right?

Where you have providers back then writing their patient notes on a piece of paper. And as we know how hard it can be to read a physician's handwriting, that takes time in itself. It's a common joke that a physician's handwriting is almost impossible to read. Right, right.

I think it's Now with the introduction of electronic medical records, EMRs, and all these other technologies that's out there that takes the patient notes, puts it in there. What is it? It's data, right? And AI is sorting through this.

It's just leveraging that it's doing the hard work for us. It's pulling like the cow, that's pulling the trolley through the fields. Like it's doing that hard work for us. And then the domino effect that you mentioned, Mark, is that downstream.

We're going to see a lot of these efficiencies turn into. Cost savings and then cost revenue, you know, like it's going to increase the revenues. I think you have a really great point there. And I, that's essentially what we're trying to say with medical coding.

Yeah. And, and once again, it doesn't remove the human from the coding process, whether it's on the entry side or on the. Reimbursement side, it really is just enhancing. So I think that's a tool that it's almost like, why wouldn't you do that?

And I know many organizations are doing things similar to mass general when it comes to coding, but I think we're already starting to see the benefit and the positive impact there, which kind of ties back to the third category here, which is reimbursement. So. And all of these three categories we're talking about in terms of AI, really, they flow together, right? It's all part of the revenue cycle and it's all a part of the reimbursement, ultimately the reimbursement process.

So how are you seeing reimbursement models adapting to include AI? This one, Mark, will be a little bit trickier because when it comes to reimbursement and as we know, payers and the, Providers, they always they have a negotiation that deals between them, right? And since in terms of AI and how new it is, it must show that the new technology must show a clear and clinical benefits and cost effectiveness to be considered for reimbursement that statement in itself. It's hard to prove because how do you prove something that has clear and clinical benefits that take time?

But what we do see is that there's a start. Mhm. And where there's a start, there will be a finish or there will be an increase of improvement. And so what we see is within the inpatient side and the inpatient settings, we're seeing things such as NTAP.

So for those of you who aren't familiar with NTAPs, that's the new technology add on payment system that was added in addition to MSDRGs, which is the Medicare severity diagnosis related groups. It's an additional compensation, additional reimbursement on top, right? To attract, um, this temporary new payments for these new technologies. It only lasts for a period up to three years.

Now, why does it last for three years is because it's typically that's the time that takes for the cost of that innovative technology to reflect on the MSDRG structure and to qualify for these and now let me explain why it's hard and wishy washy when it comes to reimbursement is because they must meet these three criterias. One is the technology should not be less than three years old. The cost to the cost of the technology should not be already covered in MS DRG, otherwise why pay the NTAP, uh, additional fee.

And then the third, I think this is the most important one is that the technology should provide substantial clinical improvement over existing technologies. So that word substantial, I think that what we're seeing is that's the main obstacle that we're seeing in the inpatient house of. What does substantial mean? Right.

Because subjective. Subjective. Substantial is, does it cure or does it, you know, is it better by this other technology by 10%? So that thing, I think that's still an area where lawmakers and regulators are trying to, and people that are, uh, involved with this whole process are trying to figure out where do we define substantial?

Now, flipping over to since we're at inpatient, flipping over the outpatient, we're seeing some success. So outpatient, which we call OPPS, right? Outpatient prospective payment system. They're using things such as APCs, ambulatory payment classifications, right?

And so these APCs are just codes where it defines what reimbursement you're going to get. But these new technology APCs, those are the bread and butter. Of the A. I.

And technology corporations that are coming in because we're seeing that examples of A company called heart flow clearly, and I'll tell them, right, is just some of the many companies that have were granted very recently 2021 2022 new technology APCs. And so, the reason why, and I'll explain is because instead of substantial clinical improvements, their, their threshold is just as long as it has clinical utility. I can understand clinical utility. I don't understand what substantial means.

So that's what we're seeing. And just to elaborate what does heart flow and clearly what these companies do, uh, they're, they enable radiologists or the referring physicians to gain new diagnostic information that otherwise wouldn't be possible using the old tactics. So therefore they're granted. These new technology, APCs Optella provided superior diagnostic performance compared to the standard risk prediction models granted new technology, APCs.

So each time they admit they meet those thresholds, they get the new APCs and they get reimbursed. So I think we're seeing that side of the spectrum being reimbursed there. Yeah. Yeah, that's very interesting.

And, and again, I'm sure since that is so recent, we're going to see more and more of those examples come to life, but what do you think some of the obstacles are in getting AI applications reimbursed by insurers? Yeah. And the obstacles, as I've already mentioned a little bit earlier, I think it falls into these kind of three buckets, right? One is that a lot of the payors still deem that the AI solution has low hanging value, low hanging fruit, right?

It's just not, it's the juice worth the squeeze. And it's hard to differentiate if that is substantial, if this and that. And also, I You know, it takes funding to go through these to create these applications. And so the payers don't want to pay that if they don't see at the end of the rainbow, there's a pot of gold, right?

So that's one of the obstacles we see. Number two is that we see that a lot of A. I. Solutions are great and marketable.

So many great ideas, but they lack the real world adoption. They don't see clinical evidence because why is that takes time when we are looking at clinical evidence that takes trials that takes many years, sometimes beyond the years where this technology five years from now will probably be outdated. So I don't like this cat and mouse game where you have one person company A is trying to create this technology but doesn't have the evidence. For two years from now.

And then all of a sudden they go back to level one and then they keep doing that over and over. And hopefully they luck through with getting their technology utilized by some clinicians. And I think number three, the last bucket is that AI solutions have, must have provide a better pathway of care, but clinicians aren't wanting to see a better improvement of efficiency because the argument for efficiency is not as powerful as patient care. So that's why.

When what all the cases that we've just described, most of them were based off of efficiency, but very few besides the radiology ones were based off of improved clinical care. So that's why there's this kind of catch up that AI needs to play. Hey, we may see that we can do this automation, but we need to improve patient outcomes. That's where the bread and butter reimbursement will, will, will happen.

So, yeah, I think that makes sense. And that's fairly. I think we've seen that before in the healthcare industry as we've evolved, particularly with the onset of technology. I remember 15, 20 years ago when there was an argument that we shouldn't bother with the burden and extra effort and cost that comes with EMRs if it doesn't, Have a, a, an impact, a true, a meaningful impact on patient care and outcomes and those types of things.

And, and I think the industry, even in that example, it took, uh, some catching up time for everybody to really get on board and see, oh, okay, wait, there are other pathways of value here. It can really help us all, but yeah, it's still an evolutionary process. Stage and we'll take time, all those things. And I do think we'll get there and we'll continue to see that positive impact expand and grow.

But I think one of the things when we talk about AI and maybe trying to look into the future a little bit here, I commonly hear when, when the subject of AI comes up, the ethics behind AI and that, that go with it. Are you any thoughts as far as ethical considerations, even risk? Yeah. Involved with using AI specifically in the areas that we've talked about today, of course, I think that's the, this is 1 of the main important questions is, what is the prospects looking like?

And what are some of those ethical considerations where, you know, what are the guardrails that we have? Um, because we can't just let this technology go crazy. It ultimately can, you know, Lead to harm more than good. Sure.

So one of that is we must understand that data is the new gold, right? We're in the gold rush of data. It is so valuable. We have companies out there that make a whole living, their whole company cost structure and how they make a profit is through the selling of data.

Yeah, let's take it back to 1939, where the first computer was about 50 feet long, weighing over five tons. But now we have a computer of a MacBook or something that weighs about three pounds and can do 10 times 10 to the 10th power of what that computer could do. So when we're venturing into this unchartered territory, I think we need to have a, a good template, right? A, a good guardrail system that guides this very rapidly expanding technology.

So at SAP, SAP, I like the way that they've, they put these three, three rules, right? One is human agency and oversight. So you want to safeguard human autonomy and decision making. So.

Making sure the human element is at the number one spot. Yeah. Number two is that you want to address any biases and discrimination because as a data analyst myself, I'm looking for these trends in the data. But as a human, I understand that this might be biased, right?

But an AI or algorithm may see it. That's not a bias just because this demographic is poor and this and that. That's not a bias to me, but eventually it leads to biases. Number three, I think is the transparency and explainability.

So we want to reduce these black box effect where, yes, it creates and produces great stuff, but no one understands it besides the developer, right? You need to open that window into let everyone see what the internal structure is. That's how we trust it. It's not just some black hole.

So I think that's just three ways that SAP has honestly created some of those guardrails. In addition to those, the Federation State of Medical Boards, FSMB, one of our vice presidents here sent one of the medical boards over. They added two more that I think I would like to mention in the podcast, which is the responsible use and accountability. So these developers should provide physicians a better With education on how to use the AI tools, right?

Because you want to make sure that those providers are accountable for these AI tools. And then obviously the last one I think is continuing review and adoption of law and regulations to make sure that policy makers are on top of the AI topics. And I see you, Mark, looking at the notes, but this is just some of the stuff that over time evolves and people are producing more and more guardrails on top of that. So, yeah, and I think we're going to continue to see that evolve.

Obviously, I think, I think those three points from SAP really. Cover it well, if we're not removing human oversight from the, whatever AI does, whatever application and there's, I've heard countless people talk about the bias that comes with some AI type application. Solutions out there. I'm not talking about any of these that we're talking about within health care.

I'm just talking general, generally speaking, some of these solutions that are available to consumers. And and so I think that can definitely be monitored and accounted for. And then I agree, understanding. How is it doing what it's doing?

And that way we can know when some of these other potential weak points might reveal themselves. So I think that covers it well, but all right. So if you had to look forward a little bit, what do you think? If you had your little glass ball there and, and you're to put a theory behind where AI is going specifically in healthcare, what are your thoughts?

And if I had a glass ball, I'd be a billionaire, but what we can say is that with the trends, is that these tedious and mundane tasks, the ones that You used to have to do by hand. They're going to be automated because you can just see that over time, right? When you look at the menu of manufacturing industry, people used to build cars by hand. Now they have machines that put the doors on, put the engine in the agricultural space.

You used to have to plant crops with your hands, but now they're literally using AI to plant those crops at the perfect time with perfect humidity and you're getting And Massive, massive amounts of more yields in your crops, right? So, What goes to show is that what we will see is that just like how those things have been automated, our thoughts will be automated. Right? So the data that we produce these intangible objects known as data that will be automated.

And that I will utilize that to its full capacity in order to help us with our daily lives. And especially in the health care business. Helpless with our task that's going to really improve its efficiency. And I think if you take the time to look back in time, the birth of the internet was not too long ago, but also in the last 20 years, that's really how much our technology has expanded quite logistically, but it will take time because healthcare has been guess what healthcare has been here a lot longer.

Right. AI is just only a fragment of the time that healthcare has been around. So that's why I think over time, we're going to see this technological boom with AI going in a logistical curve, not like a linear curve. And we'll start seeing a lot more clinicians, physicians, hospital systems implement this because it produces value.

And so that value over time will, they'll reap the benefits and the benefits of that will be cost savings and being able to obtain more reimbursement. Yeah, which ultimately does help everybody. Yes, maybe that's improved revenue for the provider entity, which is great. But if it's improving efficiencies, it's improving the quality of care, it's improving the time and access to care, then that's something that has a direct impact on the patient as well.

And on the overall cost system, if we can reduce some of these costs. What does that do to reimbursement and the cost for, you know, various procedures and various types of care? I think you have to be able to see that holistically in terms of backing up a little bit and getting that bigger picture and seeing how different benefits here and there and other places. Ultimately will flow downstream to all the parties involved.

And, and so I, I don't look at this as, Oh, this is just a way for providers to make more money or for insurance companies to. Reimburse less or build more, or maybe to make less, even one could argue, maybe look at it from that perspective. I don't see it that way. I see truly see it as no, this is something that, and just generally speaking, AI and technology as a whole, which, and this data.

Is something that can really improve throughout the system, right? And might I leave this last quote? Is that someone named Jay Aslan? He said this beautifully and right, which is that I will never replace the human effort, but rather it might augment humans and that when we look at these https: otter.

ai AI augmented systems, they can actually achieve better results than any human alone. And so I hope that our viewers can take from it is that our listeners can take from it that anything that we've done before just times that by 10 and AI can probably help you more than you think it shouldn't be scary. Yeah, I think that sums it up very well. And you're right.

It should not be scary. And frankly, we would be, frankly, we'd be foolish to ignore it and to shrug it off. The idea that this is, is going away is just, I think we need to move on from that. And instead let's focus on and work towards how it can actually make these improvements we're talking about and probably a lot of other areas of improvement that we haven't even identified yet, but, and that's ultimately, I, I, I appreciate that because that kind of brings us full circle.

That's ultimately what Coker is trying to do with our clients, whether it's. Performance optimization and performance improvement, or whether it's enhancing compliance standards, or it's undergoing some sort of financial transaction or financial improvement effort, then there, there are so many options and so many pathways that we can pursue. And I think AI is only going to play into all of those and many more areas. Hey, I really appreciate you walking us through this.

This was. Very educational for me. We probably could sit here and talk about this for hours on in as I'm sure people already are doing, but this is definitely one of those things that Coker has already for the last few years been involved in with our clients, but we're just seeing it more and more so those folks listening out there. Hopefully they got a little bit grazing of the surface as far as AI and healthcare, but we're really digging deep on this and it's something that's only going to continue to grow and become more and more pervasive.

I appreciate you teeing this up and we'll definitely talk about it more. Thank you so much, Mark. And I appreciate you having me here. Absolutely.

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