Moghis Uddin on How AI Gives Physicians More Time for Patient Care

Hello and welcome to the latest edition of the Techstrong AI Leadership Insight series. I'm your host, Mike Ard. Today we're with Muus Udin, who's the CEO for a Lithian ai.

And we're talking about, well, the impact AI is having on doctors in healthcare. Muis, welcome to the show. Thank you for inviting me.

All right, pleasure Being here. I think everybody kind of understands that maybe doctors are overwhelmed and there's a massive amount of paperwork and they probably don't get to spend as much time with patients as they like, and they're certainly not all out in the golf course. So what impact are we seeing here with AI and how is it changing the way doctors and patients interact with each other?

First of all, AI has really changed the outcome of how doctors are gonna practice now and the future. The biggest burden for doctors is like multifold. First of all, when the patients come in, they have to have the same, uh, we, we call it intake, which means that doctors have to interview or the nurse has to interview the patient.

Same questions every patient they have to ask over and over, which cause they call it talk fatigue and doctors don't wanna see a certain amount of certain type of patients per day because of the burnout from from the beginning of asking the same questions. Then they are usually used to be there where the people will simply type the notes as they're with the patient, they're doing all the stuff. And as soon as the patient needs, now they have another burden how to close the notes.

So they wait and go home. And it's called period time. And typically about three hours a day, they spend after hours just doing the notes.

What the AI has done is initially it was ascribed where the doctors can actually talk to people and it will take the ambient listening. And after the doc, the visit is done. The doctor will come in and basically look at the note, edit the note, and then send it to the EMR and still go home and go over the stuff and then close the note, saves them time, it documentation is more thorough and actually they can look into the patient's eyes while they're talking to them and making sure they're taken care of.

The third, the third part is what we come in and what we are looking at the future coming in is automation all the way through patients from home, they sit down, they take their time. The AI will interview the patient, for example is for intake, the HBI, the technical term history of present illness. How long did you have it?

Do you have a chest pain? Do you have arm pain, the arm? Do you have nausea, vomiting?

All the questions doctors ask all the time. It ask those questions before people come into the clinic. And once they're in the clinic, doctor already has the pre pretty good idea and it starts the agenda, right?

So doctor, you go to the doctor now, they don't have to ask all the questions. So they start, okay, these are things we need to discuss. So it gives dr more quality of time with patient, better patient care, better outcome.

And what else is there in the AI is like right now emerging? We are, the first one to launch is the conversational AI scribe. What it means is, as doctors are talking, it is transcribing the notes live, not ambient listening, it is generating a notes as the doctors are going through the visit, ordering labs, all the stuff, uh, CPD code, which is like diagnostic coding.

All the stuff is happening as the visits unfold. So it gives really doctors more time to focus on the patient, not worrying about what's gonna happen. Should I remember this thing?

I'm gonna go back and look at the notes again. So they actually interact with the patients more thoroughly. And once they're done, basically there's a magic edit there where people seem, doctors simply say, Hey, by the way, uh, didn't capture this thing.

I saw his x-ray and x, Y, Z was normal. So it will, within seconds, it'll just go into the notes and doctors close the note. So there's no period time, there's no burnout.

They call it talk fatigue. And also there's a click fatigue. By the way, do you know how many clicks are doctors do a day?

Just a curious question. No idea. Um, Service, say about 1700 to 4,000 clicks a day.

And what doctors call it click fatigue because they have to go navigate the whole technology, click here, click there, and it's like a racking thing. And so one, you have one window platform and all, so you don't have to do an plex. It just simply shows up.

So that all sounds better for everyone involved. But how do we get to that nirvana? Because I feel like it's unevenly distributed.

So we say, and no one seems to know exactly what it's gonna take to maybe get this into the hands of every doctor. That's a good question. Absolutely, right.

Uh, doctors by nature with technology, they, every time there's a technology, they're scared of technology. They think it might be something which has, they have to do more. So the adoption usually comes in from the word of mouth.

I'll give you an example. Uh, there was a, there's a clinic in the Midwest and the doctor said, it sounds too good to be true, so I need to talk to another doctor. So we arranged a doctor who's already using the platform, he calls the doctor and the first thing he said, are you sure it's real?

It sounds too good to be true. Finally it happened. They like it.

So adoption is in itself technology, uh, doctors that I'm not very, they wanna know how much time's gonna disrupt me, but it's a pretty good thing. Eventually I will, eventually everybody's gonna be adopting this thing, but they're not, there'll be a lot of opportunity for other folks. Everybody can streamline the process.

So doctors can be doctors. Their job is to prevent, diagnose and treat, not be a technologist, or figuring out all the technology, how it fits it. That's a future and it's coming.

Mm-hmm. So we've all seen AI kind of makes mistakes from time to time and they hallucinate. And of course humans make mistakes too.

But in the case of ai, will there be, say, another set of AI models that's checking on the work of the original AI to make sure it's right? Or how do we kind of put the guardrails in place that we need? That's a very good question.

Medical field is one of those fields where you have to be on. So any model which is trained on the internet would always hallucinate. What we have done is put the guardrails and everything, every model is trained by doctors.

So they read the process and they fine tune the model. So there're like strong guardrails, which are needed for the patient safety as well as for the accuracy of the document. So it takes time, but it does not have the internet because it is trained by doctors.

It's, there's nothing, it's not putting the data from the internet to give answers. So we have to put guard rails in place. Right.

Do you think also over time it might become possible to identify trends? And I asked this question because, you know, we have seen over the years where, uh, multiple patients in a specific region wind up having the same issues and it could be caused by something in the environment, but nobody noticed it for years because nobody had any ability to compare the notes. So, you know, as we move along here, will we be able to kind of maybe identify clusters of issues more readily because we're gonna imply more analytics?

Absolutely. There's the, the reason is there, the data is there. Do you know how many I CD 10 codes are there and CPT codes doctors don't usually use.

Take a guess how many I, we just got diagnostic codes are there. I want to, I'll put that in the thousands, 67,000 I three 10 codes. And the new one, which is an international standard, which is, it's called nomad 350,000 codes.

So 350,000 codes for explicitly each problem. For in population health, you need to capture data with precision. For example, if you have a pain, you like a back pain, left arm pain, which area the pain is, or throat or all the, we call it comorbidities, which means what are the symptoms are the similar symptoms.

And as CMS has a pretty good tool where you have to upload the data, the CMS, the problem arises. Doctors can't remember all the codes CPD codes. So they use generic codes because nobody has time to go through 67,000 codes to find out which one.

So AI is actually capturing, or we are capturing all those codes as a visit unfold. So you can do the really fine ICD can codes, which are diagnostic codes. And when you see a patent patent recognition, all the clusters, it'll be easy to manage because you have the right data, make the right decisions.

Mm-hmm. Problem before was people were not doing the coding because nobody knows that many codes. Right.

So what is a reasonable number of patients for a doctor to have if they have AI tooling? And I'm asking the question because we do have a shortage of, uh, medical professionals, but at the same time, even with ai, there's probably a limit. But, you know, have you seen any kind of numbers or anything that kind of suggests best practice here?

Uh, it depends on the doctors. Some doctors would like to have more time available. Some doctors, uh, will give you an example as we talking earlier about the HPI or the intake, what the patients do.

'cause it's, it is very thorough and what we have seen of the 12,000 initial interviews we have done, it's about nine minutes. It takes about nine minutes of interview so that nine minutes is safe for the doctor. Now doctors can use that time for that better care of the patients or they can reduce the time and see more patients.

We provide tools to make their life easy. Then it's a doctor's decision. They can easily see the uptake about 30% per day.

They can see more patients depending upon their will at if they're willing to do it. It's no secret that there are a lot of lawsuits involving medical practitioners. But will we get to a point soon where maybe the insurance companies are gonna require the physicians to have AI so that A, they can have this documentation, but B, maybe it'll ultimately reduce the number of mistakes that are made?

I think they should look into it. What we are doing is we got a license from a ME to train our model exactly doing that. But it captures every single nuance.

And it actually ref gives a reference from a ME on the CPT code, which is their proprietary models for billing. It actually points to exactly each note where it was drawn from. So the chances of error or insurance denial would be very low because it's, it is audit defensible.

It captures every CPD code. It captures I iicd 10 codes. It gives a rationale why the decision was made and why this thing is picked up.

So all the nuance which is in, uh, let me give you an example. About 30% of claims before submission. There's a biller who doesn't see the doctors during the notes, right?

So a biller has to go to the doctor, ask them, please can you add this addendum to have the proper billing? And it's back and forth. It's about 20 minutes spent per patient.

And here we have, uh, about 17 seconds. The doctors is done within 17 seconds. It generates a notes and it gives you a rationale.

And if they wanna do something, or by the way this person has via hyper uh, hypertension, which means it's over that one, it'll automatically fix all the problems and generate a node, which is like auto defensible. So these technologies are, are now being happening. People are adopting it and it is amazing.

And I think insurance companies would follow those things than they should. It'll save money to the insurance company and it will save money overall in the healthcare system. That's the biggest expense they have is the insurance company.

What role will governments play in this conversation? Are they likely to come to a similar conclusion and say to people, Hey, if you're gonna be in the healthcare space, you gotta use ai? I think that time is coming.

There are multiple reasons. First of the cost of healthcare is rising. So much doctors get paid about most time, I don't want to use the numbers, but 25% of what they produce, the rest of stuff goes into support staff, which is shortages, receptionist, billers, all the stuff.

And anything which is repetitive is being taken care of by the ai. So there would be a substantial reduction in per visit cost of overhead. So government, eventually, CMA, uh, CMS, all of them, they would realize that maybe we will, they will restructure the billing structure too.

It will save money to the system. Mm-hmm. Right, right.

Will it take tension out of this whole interaction? 'cause everybody usually involved, unless it's say a checkup, but if it's some sort of condition, everybody is kind of stressed and a lot of times doctors are meeting people probably on maybe what's one of the worst days of their existence. But can we kinda get it so that uh, the nurses and the doctors and everybody involved, um, can spend more time kind of focusing a little bit on the, uh, uh, emotional health of the patients.

Because I think a lot of the times that's as much of the issue as, as it is everything else. And it feels like to me at least the paperwork just gets in the way. Absolutely.

You're right. And there are a lot of, it's a broken system. Everywhere you look at it is broken.

Uh, for some reason, uh, when CMS, they came up with the, uh, that you have to emr, so they came this techno it people, they're really good in designing things which are very complicated. So now what doctors do, instead of practicing medicine, they end up juggling papers and all this stuff. And still it is like one thing I'll tell you, a hundred percent of time, a hundred percent doctors have their own workflow because of those juggling of the technology, which is like older than uh, I guess Windows 95.

So there is that old. So if you have a centralized system, uh, which is gonna happen because with the fire you can pull data patient data from anywhere. We, we can have the contractual summary to each person what their needs are.

It will generate the north. So doctors can be more human everywhere without emotions. There's no healing.

Emotional attachment is the important task of believing in something where the doctors care about it and and making people believe it. And it is data driven. It would not be something that is data driven.

It's honest and it will give doctors the tools they need to make decisions faster and help the whole system move faster towards that, better outcomes for the patients and the cost of delivery. I think that's the future and it's gonna be great. Right.

Of course there is no conversation about healthcare that doesn't come back to, in one way or another, cost can we take cost outta the system. And do you have any sense of maybe how much of the cost of healthcare is really tied up in these kinda convoluted processes rather than the actual care of the patient? I can tell you about, uh, just the medical side of it.

There's a lot of waste switch and the drug systems, PBMs have their own cutbacks and everybody's working on it. I know a company who was transferring the whole kickback back to the patient to produce the cost of drugs because of ai. They're building powerful tool where patient can actually buy the same drug at the lesser price than the insurance company reimbursement.

So they are things which are coming up so people don't have to worry about if its insurance is there, I have to pay only. Sometimes the copay is more than what the price of the drug is. You won't believe it.

But you know, I mean good RX is one example. They're doing it, they're giving you coupon, which is cheaper than the, you don't have to pay the copay to simply buy it straightforward. And there are technologies coming up which will solve that problem and that will bring down the cost for lot.

Another thing where we think it's gonna really make a difference with the data, as you were saying, the data clusters, uh, that would probably be where you have so much data available of if this test necess based on the total population data right now, it's like every time there's a protocol, this thing happens. You have to run those like high expensive tests when in ghost insurance, insurance deny. So when you have a cluster of data available, which is justified for reasoning, you would do the testing, which where is appropriate.

And probably insurance company would see the value of doing the testing sooner than later for the pre-authorization. So overall, the system will benefit from the efficiencies of bringing all the data together. For example, you don't have to go to different doctors just for one problem and then another one when each doctor has the comprehensive outlook into like all the things which have gone on your life.

So it'll make the decision making better and faster. And whenever things are better and faster, they get cheaper. Technology always get cheaper.

It has been there and it's always been there. If the technology is cheaper, so will be the price of healthcare delivery. That's what I believe.

All right folks. You heard in here there's of course a lot of fear and intrepidation when it comes to ai, but there's also a lot of instances where AI is clearly gonna be a force for good and this might be one of them. Hey Mogas, thanks for being on the show.

Thank you. Appreciate it. ai Leadership Insight series.

You can find this and others on our website. We invite you to check them all out. Until then, we'll see you next time.