Administrative hassles and mountains of paperwork remain some of the biggest challenges in healthcare today.
Every day, critical time and resources are diverted from doctors, nurses, and other medical staff to navigate inefficient prior authorizations and overwhelming paperwork.
Isn't it time we find a better solution?
In this episode of CareTalk, David E. Williams and John Driscoll sit down with Anterior Founder and CEO Abdel Mahmoud to explore the burden of administrative tasks on healthcare and how AI can bring clarity and efficiency to the chaos.
This episode is brought to you by BetterHelp. Give online therapy a try at https://betterhelp.com/caretalk and get on your way to being your best self.
As a BetterHelp affiliate, we may receive compensation from BetterHelp if you purchase products or services through the links provided.
Episode Transcript:
David E. Williams: Dr. Abdelmakmoud came to the UK as a refugee as a boy and then he became the youngest infantry officer in the country and then a physician. However, the mountain of paperwork soured him on medical practice. So now, as CEO of Interior, he wants to make prior authorization and other administrative hassles his own.
Invisible. Can he do it?
Hi, everyone. I'm David Williams, president of Health Business Group.
John Driscoll: And I'm John Driscoll, the chairman of Waystart.
David E. Williams: Well, before we start talking about eliminating the administrative hassle, let's talk about the month of December. Some people think wrapping up in a blanket with a mug of hot chocolate or watching a movie with family is the best way to spend the last month of the year.
For others like John, it's ice fishing or tobogganing or even riding a luge. Well, speaking of comfort, therapy is a great way to bring yourself peace that never goes away even when the season changes. BetterHelp offers entirely online therapy that's designed to be convenient, flexible, and tailored to fit your schedule.
Just fill out a brief questionnaire to get matched with a licensed therapist, and you can switch therapists at any time for no additional charge. It's helpful for learning positive coping skills and how to set boundaries, and it empowers you to be the very best version of yourself. So whether you're dealing with stress, anxiety, or seeking personal growth, BetterHelp connects you with a licensed therapist who can support you on your mental health journey.
Make it a great season. With BetterHelp. Visit better help.com/care talk to get 10% off your first month. That's BetterHelp. H E L P dot com slash care talk. Dr. Mahmood, welcome to Care Talk. Excited to be here.
John Driscoll: So I wanna know how you game the system to be the youngest infantry officer. Ramin, what? How did that work?
Abdel Mahmoud: Yeah, no, I was gonna say with that generous background that you gave, it kind of makes me look like I can't decide what job I actually want. , which is the way my parents put it. How did I, I think I just applied before going to university in UK. I kind of applied to go to Sandhurst. I grew up on, on kind of those books.
John Driscoll: And just so folks have context, Sandhurst is the, is, is the equivalent of West Point for the United States. It's, it's, it's the, you and Winston Churchill.
Abdel Mahmoud: Yes. It was, it wasn't in my class. So just, just to be clear for any of the viewers following just by audio.
John Driscoll: Well, that's what, that would make you very young.
Abdel Mahmoud: But Yes. Yeah. Now I thought for a moment, you're going to ask me, was it West Point better than Sandhurst or Sandhurst better than West Point, but I'm glad we didn't go there. But yeah, no, I applied to go to military. I kind of thought it will teach you some life skills. It definitely did. It taught you how to kind of also stay up throughout the night and execute and kind of perform on very low energy reserves and everything else.
And it's actually been one of the most helpful skills as I kind of started on the founding journey. But yeah, it was just more of a kind of something to prove. I think that was, that was the, the main
John Driscoll: I would like you to continue this because David's the only one on this who hasn't been through infantry training.
And so he's feeling more and more insecure, but David, perhaps you'd like to talk about the clinical journey.
David E. Williams: Well, I was going to broaden it a little bit, John, because it's certainly impressive. And I was, what I was trying to do with that intro really is just to compress a bunch of things that had happened.
I mean, even from the time you were, you know, a child and my broader question is you sort of your personal journey. How did that lead you to this point? How did it all come together?
Abdel Mahmoud: Yes. So I think, I think, you know, after one or two years, I did have to leave the military with an injury. So I kind of had to go pick a profession and I luckily had some good grades.
I went to medical school, which in the UK is an undergraduate thing, right? So you can start pretty early, but I think like many physicians, I think you very quickly have this realization of going in, you want to kind of save lives and make an impact. Then you very realized very quickly the way healthcare set up The impact you could have was less about your clinical skills and actually more about the environment and the tools you had access to.
I think that realization set in very quickly. I've been kind of doing stuff with computers growing up. So I had a bit of a knack for technology and programming and I felt, wait, you're calculating morphine doses by hand. I can build a calculator and we can use that. And that got me started down the kind of technology and healthcare route.
Took a year out to do a computer science masters at the same university, and that's when it really started. The avalanche couldn't be stopped, so to speak. I was going to disappoint my parents and never actually practice clinical medicine. I joined I spent some time at Facebook, or now it's called Meta, applying some healthcare technologies.
It was chatbots, but five years ago, so you can imagine they were pretty terrible and gimmicky. Then joined Google for a stop. But what I saw at Google was what led me to what I'm doing now at Anteria kind of tackling health administration and the burden of paperwork. And the main thing was, When I was at Google, I wasn't working on this directly, but there was this corner of Google that was excited about this thing called large language models.
And I remember there was one thing about them in particular that was fascinating, which was their ability to handle huge volumes of unstructured data. I kind of didn't understand too much of the other stuff. But that bit felt interesting because if you looked across all of healthcare, it wasn't that healthcare wasn't digitized.
It was digitized, and it had a lot of software, but the software didn't speak the same language. It was a lot of unstructuredly structured data where we kind of rely on expensive humans as the interoperability glue. And it felt like there was going to be a moment where something could change. It was with this kind of large language model where you could plug that into top of all the investments we've made in IT and infrastructure and maybe finally have end to end workflows.
That are fully automatable and I felt like it was a moment and that's kind of what led to the founding journey, at least the idea of it turned out, you know, there's a lot more discoveries and pivots
John Driscoll: and iterations. So, I'll be for those who aren't kind of burst in computer tech work with Greg Corrado, all the other great.
Talent that Google had and don't understand that Google really started with a, with a view with the founders believed that they grabbed enough information. You could actually make. Artificial intelligence or insights at scale and speed that we can't imagine that that it was rather than search was the plan that this large language model stuff sort of makes sense.
But can you explain what a large language model is?
Abdel Mahmoud: Yeah, there's a lot of ways you can slice this question. That's probably gonna be much better explanations out there. But the way I think about it is a eyes kind of bit of throwing around for a while, right? And then last language model is a subset since 1956.
John Driscoll: Oh, I heard somewhere that AI is basically just the edge of what computers can do today, right? And it will continuously keep shifting. But yeah, so like I think AI has been in healthcare all the world for a while, right? And I think those kind of applications generally be what's considered narrow now, which what it means is you kind of have a, a question you want or something you want AI to do.
Abdel Mahmoud: Maybe, you know, recognize hot dogs, right? Computer vision. So you train them from previous examples of data that's been labeled with hot dog and not hot dog, for example. That's kind of a classic example from TV. And you get these narrow systems, but the problem is like the things that actually humans often encounter is like, you know, General, general problems where you have to kind of pull on a few different things and reason through it and break it down.
It won't match necessarily always the label data, right? Everything we do in our stuff, like life, that's new. Well, previous label data, but we still are intelligent. And I think the thing with large language model was, what if we didn't kind of bake in these heuristics or control it and just let it loose on all the data that exists, like trillions and trillions and trillions of gigabytes and get the system to kind of almost like learn, you know, The heuristics and the rules and the patterns and what we can call intelligence.
And if you do that, the humongous amounts of compute and data training volumes and length of time, you get something out of there that what is now chat GPT and feels eerily human, like it's not human, but it feels like it's kind of intelligent and you can reframe a question a million times. It won't break.
Whereas narrow AI, for example, it would break if it wasn't in the right format and the right structure and what it's seen before. I often kind of use that as the medium between narrow in general and large language models are more general.
David E. Williams: So let's talk about what you're doing with anterior and I get the kind of the general view of it, but let's go beyond that.
So we talk about this is 950 billion burden of healthcare administration. I guess we'll be rounding it up to a trillion by but shortly. So we've got that. I think people know it. It's big. There's a lot of frictions involved in it, and you've got this idea that you're going to make things like prior authorization invisible.
Now, if I take what we're just talking about the large language models, I know that. Okay. You know, two years ago, I couldn't have said, Hey write a report about this, that, or the other. Now anybody could go to chat GPT and get like a reasonable draft from that. Like it's completely night, night and day.
Are we talking about something similar here when you're dealing with some of these things like prior authorization, which is the first use case that you've picked, can we actually see something comparable where it goes to being from this incredibly thing we could never deal with to something that's actually.
Abdel Mahmoud: Yes, not immediately, but definitely yes. We're very, very optimistic about that. Now, you know.
John Driscoll: The 950. I mean, if you weren't, you wouldn't have raised money.
Abdel Mahmoud: Yes. Yeah. You have to, you have to promise optimism. I think you have to almost to the point where you believe it. But no, I do think that, you know, this burden of healthcare administration has just been going on for almost too long.
It has to, it has the close, you know, the solution must be nearby. But I think what it is, if you take a step back, right, this 950 billion, It's not just pure waste, right? It's actually people doing work. It's, if you look at it, it looks like economic output or activity. But I think if you look at the, break it down, right?
There's many different areas you can slice administration. Some of it was on the provider side, some of it was on the payer side. Some of it straddles both. And I think prior authorization, where we started, felt like it was a pain point for both sides. There was a lot of regulation coming in to try and actually get us out of this.
So it felt like a great place to start. So we'll use the example of prior authorization. A prior authorization means. Prior permission from someone, right? So in kind of a lot of people here may unfortunately be quite, you know, well first in the prior rules, but you know, you want to get a knee surgery or something and the arthroscopy, 000 procedure.
So your physician, your provider may want to ask your insurer, are they okay with going ahead? What the insurer is trying to do is you. Hey, is this medically necessary spend? Have you tried conservative therapy that is often successful at much better rates sorry, like much better outcomes for you and much cheaper.
So it's actually a good thing, right? But the problem I think comes with prioritization is how does the insurance company get to understand if something's medically necessary or not? And especially if the medical information that being sent over is a 200 page fax PDF, right? So the only way you can do that today is to hire really expensive, is.
Jargon understander called a doctor or nurse or pharmacist to sit down reviewing faxes as a full time job And I think that's where the expense comes from because if AI could do it It wouldn't cost this much and it happened in a fraction of a second And I think the thing we're working on is AI doing it.
John Driscoll: Got it. So when you, when you, I mean, by the way, David's really good at jargon. Just, just, just, just, just if you need a human to kind of be human in the loop for the, for the, for the agent, as you think about what you're trying to do, what provides the specific application intelligence that solves the managed care riddle.
The large language models were all trained on generalized. Largely internet or print data often, you know, the game in in for in managed care is figuring out. How do you automate something where even the logic changes on a regular basis?
Abdel Mahmoud: Yes. I think there are a few components to that to that question. So we'll tackle them kind of bit by bit.
The, first component is like, how does large language models be able to deal with the jargon, right? So it mainly, yes, most of the volume of the data on the internet is non medical, should we say, right? Maybe one or two percent is, and the thing is surprisingly, actually, even with that amount, you can get chat GPT parsing the USMLE than if you saw some of these kind of you know, scary sounding
John Driscoll: things.
Yeah, GPT 4 did a nice job. Yes, exactly. Each, each chat, each generalized model upgrade from chat, it, it, for an open AI gets materially better in the more, the most, the more recent paid version actually passed the general board.
Abdel Mahmoud: And even if you look at the US Yeah, exactly. If you look at the US media questions, they're not easy questions.
They're actually pretty, pretty hard. And, you know, whether someone gets a, a knee arthroscopy or not, it's actually a bit more of a simpler, simpler question. So the question is not a can the u the large language models understand medical jargon? They already can, whereas previous attempts at AI, you had to kind of spend a lot of time just training it on medical records, medical data.
At this point, you can take a large language model off the shelf. And I think some insurance companies have tried to build some of this in house. So that's the first part. The second component comes from like you, like you touched on John, which is the guidelines changed very frequently. I mean, there's a scary statistic.
I think at the Harvard business-review that medical literature doubles every 72 days.
John Driscoll: Yeah, about right.
Abdel Mahmoud: And most physicians are up to date. Actually, the average takes about
John Driscoll: 17 years before when the academy actually agrees on a novel innovation that it actually becomes standard of practice across an entire specialty.
Abdel Mahmoud: Exactly. But the real world implication of guidelines changing so frequently is we never digitize them because the minute we digitize them, because building rules before so complex and involved a lot of expensive labor, you just print it into a PDF and give it to a nurse to apply to a PDF record. Right?
And what we've said, actually, you know, the dirty secret of a lot of AI companies is it's not the reasoning part. It's actually if you can take that data and make it digitize and structure it to feed it into a system is where you take a lot of the work. Because if you look at a nurse, right, you know, answering a question of, you know, is there evidence of the failure of conservative therapy for at least six weeks?
It's not a hard clinical reasoning task. It's an information finding task, right? And if you have a few hundred pages, a lot of your brain power has not been used in thinking, it's been used in finding. But large language models are very good at that. They can find those instances in a second, and maybe they may not be able to fully reason yet.
And I think they're making good breakthroughs there. That in itself leads to the results that we see today, where a nurse goes from an average of seven, sorry, an average of about 10 to 12 reviews a day to now 20 to 30 reviews a day, right? Cycle time reductions of 77%. And all that we're doing is just helping them find information in the facts, right?
It's not even that breakthrough. Obviously, a lot of engineering goes into how do you structure that facts and make sure that there's no hallucinations and things like that. But that's how low hanging the fruit is.
David E. Williams: So I noticed that your team seems to be quite clinician heavy, which surprised me. A little bit, because I was trying to understand what part of the problem you're, you're going at.
So I'm interested in both your philosophy on the kind of people that you're hiring. And then also, what do you add from a technology standpoint, given that the LLMs are out there and, you know, continue to advance at great, at good speed.
Abdel Mahmoud: Absolutely. And I think the main way we think about this is from what are we doing?
Where do we live in the stack, right? Of AI houses, a foundation AI companies on one side, right? And like, you know, United Healthcare or Kaiser Permanente, completely healthcare company on the other side. And it's, we're trying to bridge the gap of the frontier of technology into solving domain kind of real world domain.
And you can't build technology without having the domain expertise. Right. And usually what happens with a lot of companies in the last 10 years is they're often in silos. So they may bring clinicians and engineers and put them apart. So initially RCFOS one or two, three hires an engineering fund.
Some of them had MDs who then retrained as computer engineers and worked as machine learning engineers. It's not a scalable hiring strategy. Trust me, we've tried. There are a few that you hand select, but it meant we set a culture where engineers and clinicians work side by side. Today. Our engineering team will have nurses, utilization management nurses in the office next, next to them working together and then each being comfortable with each other's jargon, so to speak.
Right. And I think that's been really important in us to map the domain that will map the technology to the domain that leads us to kind of outperform. And I think, I think that's our speciality is to bridge those two worlds.
John Driscoll: Are you, when you think about The models, the foundational models, the large, massive trillion bit kind of monsters, are you using one of those models and then tuning it?
Are you using right? Maybe walk through how you're taking the powerhouse models of a meta, a Google, a Microsoft open AI, and then. Are you building a smaller version of it yourself, or are you tuning one of those and applying it just so folks understand we're a little bit more technology forward, unlike David and I kind of how it all works, how those pieces, those puzzle pieces fit together to solve some pretty quotidian problems, like how do you get paid?
Abdel Mahmoud: Yes, yes. You may not understand the technical jargon, but I didn't know the meaning of the word you just said, so I'll have to say that.
David E. Williams: It was just practical. It was his pronunciation of it, I think.
John Driscoll: Exactly. It's, it's a few, too few, too few years, a few many years of Latin.
Abdel Mahmoud: Yes. So the, so yes, from, from a technical answer, I think those, the words you said there, it's actually a mixture of them.
That's the answer. So the first approach that people will generally do, and sometimes even like innovation labs within an insurance company or a healthcare provider will be, just to use a large language model provider off the shelf. What you'll find is, for like writing marketing copy, website copy, whatever, you get good results, but actually you get pretty terrible results for anything more complicated.
A bunch of clinical questions in a row and the errors stack up in a sense. So the next separation iteration on that was, okay, how do we use a mixture? In kind of in a constellation in a sense, an orchestration, which is, Hey, there's a model that specializes like structuring the medical record and extracting dates and extracting metadata.
Another one generalizes kind of all focuses on, you know, how do I structure guidelines to know what to what question to ask at the right time? Another one to just focus on question answering, given a question and some evidence, what is the right clinical answer? So these, you get kind of these abstractions on top of these large language models, and then you can do techniques such as.
I think RAG, Retrieval Augmented Generation, in particular, for those who don't know, it's getting a bit out of date because I think in healthcare you need a determinism, of be able to replicate questions, so you really don't want to be using that too much. I think it's good for MVP or proof of concept.
But then there's other things like distillation, so once you've run a model, multiple times, you can actually take all that knowledge in the, in the example of data you have to train a smaller model to be a hundred times more efficient, a hundred times faster and even more accurate actually. So these are all the techniques we're employing.
And to give you an idea. And
John Driscoll: that particular training, the smaller model through distillation, I think really is the future for applied specific models in things like healthcare.
Abdel Mahmoud: Absolutely. Because if you think about it, it's like, This GPT 4, it's like it's got the world's knowledge in its head, right? And you need just the corner of that brain that's focused on, you know, knee arthroscopies or MRIs or something.
And what you want to do is you want to, you want to get that part out and then just ship away the rest, right? So you can be way, you know, 1 percent of the electricity used as opposed to 100 percent of the electricity.
David E. Williams: So who is Florence and what are you expecting of her?
Abdel Mahmoud: Yes, Florence. So there is a famous Florence called Florence Nightingale, and this is her namesake.
So when, when we have our AI working, right, it's really just a constellation of model calls, like I said, maybe up to 15 different large language models working together. But sometimes when you're, when you're working with nurses and Florence is putting results, we saw AI spitting out results. We actually gave it a name because we found actually a lot of the users can almost.
Bond with it in a sense of, even when giving feedback, Florence made a mistake, or, you know, the user experience, because then there's dashboards and software. Sometimes you want to differentiate between the areas that, you know, the UX, like a bug of logging in and logging out, or the PDF not loading, and actually AI reasoning, almost like a colleague making a mistake, right?
So we've kind of anthropomorphized it. a lot of opinions about anthropomorphization of this. AI, but we found that nurses actually love it. But there was this one moment where, where one of the nurses is out on leave for a week and it's like, Hey, just take email bus, the customer kind of helpline and be like, Hey, just let Florence know.
I'm just out for a week and not to be too upset. I'm actually in a holiday. It's really funny. Things like that, that kind of make it more lovable.
John Driscoll: And, and, and to be fair, Abdul, that is actually one of the weirder aspects of robots as well as that, that, that have any sort of anthropomorphic or frankly, like animal, like robots or.
Bots where there's any sort of conversation is they are quickly, comfortably anthropomorphized and it's, it's, it's just is, it's not, it's, it's, it's, it's, it's been very consistent across, across, across many cultures, actually. Yeah. So, so when you think about, you know, solving the prior authorization problem, which may sound small to those who are not involved in trying to solve it, but it is a pretty material part of the.
Yeah. The ecosystem. I will. I said the bad news is as chairman of Waystar that we do revenue cycle software, I will tell you that healthcare companies have insurance companies have gone from denying or complicating prior authorizations to now making them easier to get authorized and they're just denying the claims.
How do you, how do you think about the, the future of, and I speak from like real time experience with certain payers. As we're, as we're, as we're just reprogramming our own, our own algorithms, how do you think about the future of interior?
Abdel Mahmoud: Yes, that's a great question. And I think I think even like, you know, just to touch on recent sad news, right?
With with the UHC situation. And then the way the public reacted, and I think, you know, you could easily say, well, that's, that's evil and people shouldn't be reacting like that way. But there is a sentiment there, and I want to kind of touch on that sentiment of people are frustrated at the end of the day, right?
They feel like they're paying for care and that they're not getting it right. And I think the way we see and why we get out of bed in the morning, right, is not to work on prior auth for prior auth's sake. I think why we do that is how do we reduce the friction? I don't think providers are evil or payers are evil, right?
I think it's very easy from a shadow level to be like, Hey, that group of people there just want to deny care. I think it's, it's more incompetence, right? It's more of like, there is a right answer. That's very hard to get to. So you have to apply heuristic rules because you also have two options. Either spend You know, if you could hire a million nurses, payers, if for free, right, if you could hire a million nurses for free, payers would hire a million nurses to go through every single thing and make sure the right care is paid for, but that's just too expensive.
So you have these kind of like weird rules or AI doing it, that's pretty poor. What we wanna do is, and that's why we believe in agentic ai, AI kind of computers that feel like human. It's almost like you've had a human going in detail through that person's medical context and medical necessity and giving a thoughtful answer.
To that, right, as opposed to just having a black box, heuristic AI trained on a million examples like you, but not you, right, you know, people from your zip code, your ethnicity, working for this care, denying
John Driscoll: it just, just folks are tracking adult. I mean, the, the, for those who aren't using the models, agentic AI, the agentic system is really thinking about bots like.
Florence as co pilots or supports or leveraging them as sort of sitting beside the clinician, the decision maker, the patient, frankly, and helping them navigate, dominate. Or liberate their, their help themselves from their healthcare journey, but the agentic world is, is, is, is a world where these models become or, or, or, or, or formed or self formed as partners in leveraging technology to bring intelligence to the point of care or insight.
Abdel Mahmoud: Yes, yes, I always use the analogy of, of credit cards. You go abroad. Somewhere nice and sunny and you tap your apple pay for a coffee, right? You get it instantly the equivalent in healthcare is your bank saying wait three days I'm gonna call the other bank and agree what currency and whether you have you know Whether this is forging or not and give you an answer in two weeks saying oh you couldn't get the coffee Right.
It sounds crazy because it is and I think in healthcare What the ultimate goal is when a provider wants to do something they get an instant answer about the health plans view on whether that's medically necessary or not. And 95 percent of the cases that are approved, it's just, it's a pain involves a bunch of units, right?
We could all be doing better things.
David E. Williams: So John just assumed away prior authorization, which is a huge problem, but you've also put on your your roadmap, where are you going to go beyond that? And I noticed things like risk adjustment, care management, payment integrity. And I'm wondering what's the logic behind that path.
And these are all areas that are actually, They have vendors in them. They have, you know solutions and how, how are you going to differentiate from what's out there? Exactly.
Abdel Mahmoud: The way we see it is often for a health health plan, right? Health plan is a business, really. They're not in the business of providing care.
They're in a business of understanding risk and understanding their clinical population, right? And then can be able to underwrite that, right? And I think it's therefore clinical data powers a health insurance company, right? To be able to do great decisions. Now, if that clinical data is unstructured, what happens is that clinical data usually comes in.
The first port of entry into a health plan is their prior authorization. And that gets just stored as facts in PDF. Maybe you give a decision to someone two weeks later about whether a prior auth, but it just sits there in a data lake or whatever for like two years before it gets sent off to a risk adjustment firm to do it or sent off to payment integrity.
What we want to do is like, how do you turn that data into action immediately? The minute it comes, not just for that prior auth, so that, so the member or the patient gets a decision, right. But it's like, Hey, we also noticed that there's an AF in there. diagnosis there. Do you want to go and flag that for care management and case management?
How do you become kind of more proactive earlier? We noticed some missing codes that you haven't captured and that's the pay is right, right? They've taken on that risk. They should be paid from the government, but not two years later, immediately. And then the other component to that is we don't see ourselves competing with existing software companies.
There's a lot of systems of records and companies that do great work from case management software to core administrative platform. We see it as more of like trying to Build the employees, right? Why are nurses and doctors
John Driscoll: David and I may argue whether they're all doing great work or not. Well, some are, some are John.
Abdel Mahmoud: Yes. But would you wager that 80 percent of it is like stuff that even the employees themselves would say, this is like, why am I doing this? Why am I logging into 30 different systems and reading all these PDFs? Right. And I think that's, that's the goal here is we're trying to build the employee that uses the risk adjustment suite, right?
We're trying to build the agentic feature for that. So. You know, health insurance companies don't need to be 60, 000 employee base, right? It doesn't make sense that it should be.
David E. Williams: John, last question to you.
John Driscoll: I guess like if you roll the roll the tape five years forward, broaden the aperture beyond your company to perhaps the companies that are posterior to your anterior, what, what, or, or, or, or, or, or, or next to you.
What's the promise of AI in healthcare? And, and, and if you were to paint a picture for those who don't know AI intelligence, healthcare rags, large language models, NVIDIA chips versus us renting all the technical mumbo jumbo, what's the, what's the promise of AI and healthcare going to mean for patients and doctors and taxpayers?
Abdel Mahmoud: Yes, I think immediately it's what our mission is to reduce the burden of 950 billion dollars of health care administration. I think that that should go down to at least 200 billion to 300 billion. I'm sure there will be some, but it's that kind of 80 percent chunk that is really just You in humans as interoperability, right?
A lot of it is labor, right? think a lot of that labor, by the way, we have a shortage of nurses and doctors. They shouldn't be doing a lot of this administration.
John Driscoll: Just to put a point on that, we'll be about a million clinicians short over the next two or three, you know, three to five years.
Abdel Mahmoud: Yeah. And the idea that some of them are just full time jobs looking at faxes, right.
Doesn't, doesn't sit right. So I think just from a high level, that's, that's what it looks like. But I think for the, for the, for the patient, I think it should feel like, And I kind of find it funny. I'm using finance as an example, because often finance is seen as a very slow moving industry. But it should feel like Apple Pay, right?
It should feel, accessing care should feel like instant for providers. And just you get on with it. You can focus on the stuff that's happening with your care, right? It's just the more important stuff, not in paperwork and back and forth for claim edits and prior authorization and so on. It should, healthcare should just feel like it works, right?
That's it.
David E. Williams: Well, speaking of that's it, that's it for another episode of Care Talk as well. Our guest today has been Dr. Abdel Mahmoud, founder and CEO of Anterior. I'm David Williams, president of Health Business Group.
John Driscoll: And I'm John Driscoll, the chairman of the Waystar Corporation. If you liked what you heard or you didn't, we'd love you to subscribe on your favorite service.
And thank you, Dr. Abdel.
Watch the full episode on YouTube:
ABOUT CARETALK
CareTalk is the only healthcare podcast that tells it like it is. Join hosts John Driscoll (Senior Advisor, Walgreens Health) and David Williams (President, Health Business Group) as they provide an incisive, no B.S. view of the US healthcare industry.
FOLLOW CARETALK
댓글