The Free AI That Pulls Your Entire Medical History and Connects You to a Real Doctor

The amount of medical knowledge that exists doubles every 7 months in the world. So there's no way a human is able to keep up. 100 million Americans don't have a primary care doctor still, and we spend $1 trillion on— If you can do a couple of things, which we're doing all of these, by the way, which are make the doctor 10 times more productive, collapse the cost of care. So it was sort of the convergence of all these things for me that was like, hey, now is the right time to build this company, even though I've been thinking about it for Shark Tank shark Daymond John recently introduced me to the company Lotus AI, and my guest on today's show, KJ Dhalliwal, is the founder and CEO of Lotus.

They're up to a pretty cool project, uh, to give 100 million Americans world-class primary care for free. By using AI to basically collapse the cost of care and make it personalized and available 24/7 via an app and also make physicians' lives a lot easier too. So, the shownotes are at BenGreenfieldLife.com/Lotus. I'll also link to Lotus's website and app right there at BenGreenfieldLife.com/Lotus. Let's go talk to KJ. Alright, KJ, you and I actually have not talked before, But multiple people have sent me a heads up on what you've been up to in the whole AI meets health realm. So, I would love to start here. I actually kind of stalked you for a while. I went to the Lotus website. I saw there that you said that you saw firsthand how broken healthcare is. So, that kind of made me curious. Did you have some kind of experience in the medical system that soured you?

Yeah, definitely. It's one of those things that I've been sort of thinking about for a really long time because as a child, I was an immigrant translating doctor's visits for my parents. And sort of you realize all the inefficiencies and all the things that are wrong about American healthcare. And when I was 18, I had 2 disc herniations from a sports injury. So, that sort of like threw me down a rabbit hole of sort of years of specialty visits. I would literally carry around a binder of my physical sort of health records and they would give it to you on like CD-ROMs in terms of your image.

Ben Greenfield: By the way, just to interrupt you real quick, sometimes I wonder if we are in the dinosaur age because I'll get— when I get an MRI, for example, I will still get like the CD mailed to my house and I sit there scratching my head. I'm like, what do I even put this in?

Yeah, I don't even have anything I can put in anymore. Exactly. And yeah, I mean, that's a part of the problem, right? I think the, the industry is always, uh, sort of gatekept, right? Like, a large part of that is, is causing people to get the sort of retesting over and over again. So you end up with all these sort of records in all these places, and, uh, there's no way to sort of like unify it. And that was sort of most of the arc of my life, was seeing as a patient firsthand, but then as a child seeing my parents get misdiagnosed and struggle to sort of get care. And then my sister ended up actually becoming a physician out of that. But then I saw the problems from the physician side through her as well. And it really wasn't until I sold my last company, which I built, was called Dilmil. It's the largest South Asian dating app where I really learned how to build, you know, like consumer products that people trust and use on a daily basis. And after I sold that company, really started thinking about this problem again because it was something I always thought about, but AI wasn't there and healthcare data interoperability wasn't really there. And so a lot of things in life are about timing, and, and this is kind of why we're— I would say the why now behind Lotus AI and what we're doing today is really the, the genesis of it was probably the seed was planted when I was a child, but it was really when, you know, AI came about and as well as health data interoperability and and being able to sort of pull the data but then use AI to reason over it to do things that physicians and clinicians do to help them really be more productive. I would say those are the 3 sort of converging forces. And the other big part is obviously like Americans have lost trust in the sort of institutions and the system, and people are really at a breaking point, right? 100 million Americans don't have a primary care doctor today. So it was sort of the convergence of all these things for me that was like, hey, now is the right time to build this company even though I've been thinking about it for a really long time.

Yeah, I think for a lot of people it's kind of like a black box when you go to the doctor in terms of scratching your head about how the doctor is talking to say a hospital or let's say an imaging clinic. I have a dashboard, I've actually lost track of the number of dashboards I have that I log into and have some medical records that seem to be scattered across different practitioners, hospitals, clinics, et cetera. I don't know if you know the answer to this, but like even just big picture overview, how do digital medical records actually work? Like when you go to the doctor and then maybe you go to the hospital like 3 blocks down the street, how are they talking to each other?

Yeah, that's a great question. So, a lot of times they're not talking to each other., but the way that they're trying to do that today, and it's, it's sort of a pretty complex answer, but so each EHR vendor, there's companies like Epic and, you know, certain—

EHR is electronic health record.

That's right. Yeah. Electronic health records, which really came about in the last 2 decades, mostly like in the last decade where hospital systems were sort of incentivized from HHS and CMS to move to electronic health records. And because before that it used to be all paper, right? Like you would go to the doctor, the doctor would take the note in a paper and put it in a file somewhere. And so there was really no sort of digital version of that. But then as records got digitized, these hospital systems sort of built up these walls because if you think about it, when you're a patient at a hospital, it's, or a health system, it's in their best interest to try to keep you as a customer to keep coming back there. So if they make that record retrieval super easy for you, then you could, the switching costs go down and you can go anywhere and get care, right?

So, it's that nefarious. It's not just like, I guess, what's the saying about don't attribute to ignorance or something like that or that don't attribute malicious intent to ignorance or stupidity basically, but it actually is like a marketing mechanism to make your— making your records largely inaccessible to ensure you're coming back to the facility.

Yeah, well, I mean, my point is there's no incentive for them to really push on trying to make it super accessible and interoperable, because if you think about it from a business perspective, for them, they want to lock you in as a customer and keep you coming back. So it's, it's easy— so the switching costs are, are higher, right?

Yeah. Okay, I get it.

Incentive— that really hasn't been the focus of a hospital system or even the EHR vendors, quite frankly, because The customer of these electronic health record systems is the hospitals and the health systems usually, right? And they'll do whatever their customers want to do.

So, in a nutshell, putting health data into patients' hands or allowing for there to be a nucleus that multiple clinics, physicians, or medical providers could access has not been something that's in the best interest of your medical provider up to this point.

No, because all of American healthcare is sick care today and they really, once you, you know, you get sick, you go and get treatment, they, they bill the insurance and, and they get reimbursed. Outside of that, there's really no one that's really incentivized to get all the data from all these siloed, sort of fragmented sites of care that you've interacted with over the course of your life and then stitch that together and then actually present that to you in a fashion that you can actually understand it, right? And there's obviously like, there was emergence of things like value-based care, for example, and other organizations that are trying to sort of do a bit more preventative stuff. But again, because the system's so deeply entrenched and it's largely a reactive sort of sick care system, no one's really pushing to really help the patient get agency over their data, right? But there are these sort of like programs now under, you know, HHS and CMS where they're really pushing to get people, you know, systems to sort of commit to being able to make data interoperability more easier and accessible for patients. And so we're, you know, there's these systems called HIEs now, Health Information Exchanges. There's a new legislation called TEFCA, which really tries to sort of incentivize health systems and participants to share data. So we're pulling from some of that as well, but we're also directly integrating to all the EHRs as well. So it's a very messy system today, and no one's really built something until Lotus AI came about where you could sign up as a patient, verify your identity, and we would go and get all your records for you in minutes. So that was the sort of large innovation, the big innovation that we did.

When you just gave that brief description of being able to sign in and pull your records from anywhere, I guess it kind of opens up the discussion about AI because I think the way that people think about AI meets healthcare or AI meets medical records right now is I've got Claude or GPT or whatever in front of me and maybe I've researched or Googled or asked an LLM itself about good prompts for, let's say, reviewing my labs, solving a health issue for me, being a modern fancy version of Dr. Google. And possibly if I'm really advanced, I've uploaded all my labs from the past 10 years and and gotten a really good kind of a really good result when it comes to actionable information. What you're describing sounds like something different, not just some kind of an LLM or AI-based model to make you sleuthing your own health easier, but something that's actually digging into your previous medical records and somehow interacting with the medical system?

That's right, yeah. There's sort of 3 major components, right? First is getting the data, which is getting easier now, but like to your point, people have sort of access to their lab work. Maybe they went to Quest or LabCorp.

That's great.

They can upload some of that. They can log into sort of what's called their MyChart portal through like Epic, which is the EHR vendor that works with a lot of the major hospitals. Getting that data is the first part. And then a lot of times that data is messy. It can sometimes actually be even wrong because of a human error. Somebody puts something wrong into an EHR.. And so it's cleaning that data, structuring it, that then our AI systems and our physicians and our clinicians that we work with can actually utilize that data and then actually giving you like treatment and care based on that. So we are not only pulling data in, but we're actually a fully functioning medical practice that's able to give you care that's grounded in your sort of longitudinal health history as well as the latest evidence and research by actual real human doctors. So we always say we're building the best AI doctor powered by real doctors and the latest sort of evidence and all of your data. So that's sort of the overall summary of what we do. But obviously, as you can imagine, there's a lot that goes into each of those sort of what we built.

Yeah.

And by the way, it's not just medical records like that we're pulling for the last 10, 20 years that you've seen doctors, but it's also your wearables data, right? Genetic testing data, your insurance claims data. So if you have like, you, you know, a wearable like an Oura Ring or Fitbit or Whoop or, you know, 8 Sleep, like we're able to aggregate all of that data in as well. And no one's really brought sort of the medical records and this sort of contextual health data into one system and normalize it and standardize it and then actually given that to AI agents as well as physicians to be able to give care based on that. That's sort of the big innovation that we sort of have done and then. And that's why it's super exciting because we can do, we're doing this by the way, all outside of the existing healthcare system. Like we don't need to partner with the hospital or a payer or like an insurance company. So we can go direct to the patient on one end and then we go direct to the physician on the other end. So we built two systems basically where a physician uses it and the patient uses it and they're able to sort of interact and, and just get world-class care.

Okay.

Let, let's, let's dig into that a little bit. For illustrative purposes, I would love to know, and we've got time to unpack this, as the consumer, as the patient, what happens when I download the app in terms of the next steps? And then, I would also like to rinse, wash, and repeat that from the physician side.

That's a great question. So, as a patient, you download the mobile app, on the App Store. It's called Lotus AI. And then once you sign up, you verify your identity, you know, your name, your date of birth, your address. Then we do ID verification, or you can use your Social Security. And then once you do that and you sign the consent to treatment the same way when you would walk into, you know, a new doctor's office and sign the clipboard, we do a digital version of that. So then that's called a consent to treatment. When you give us the consent to treatment, we effectively become your doctor and your provider. So that then gives us the authorization, almost like a power of attorney, to go and get all your records for you. And so we've built the digital programmatic endpoints to do that. So what happens is within minutes, we're sort of hitting all these sort of endpoints across the country, aggregating all that data, and then we're basically doing something called ETL. And it's more of a technical word. It means extract, transform, and load. And what that means is you take the data, you clean it, you do duplicate— deduplicate it, you structure it. A lot of it's unstructured data, some of it's wrong, so you make it accurate. All that's happening in the background. So as a new user, as a patient, now you have all of your sort of health history at your fingertips. And then you can optionally connect like your wearables data. So you can sign in with like Oura, for example, and bring that data in.

Okay.

And then, if I have data that maybe doesn't exist in the electronic health record world but that I happen to have on my own, let's say I randomly got a full-body MRI or a DEXA scan or some test, I don't know, like a SIBO test or a genetic test or something that might not be already available online, can I directly upload that?

That's right, yeah. You can upload any. Our goal really is always to try to make it as easy as possible for the patient so you don't have to go and find the records and upload them. But of course, like, if you've gotten something that's outside of the traditional sort of care system or even a wearable device that has API access, you can, you can of course upload the data yourself. Obviously, there's the Apple Health sort of kit integration as well. So like, Apple Health data comes in pretty seamlessly as well. But, but yeah, so you bring all that data in and. And then you're starting— then what you do is you start to see insights about your health based on all the data. And then you can ask any question to Lotus AI and it will sort of take the context of your entire life's worth of health data and all the latest evidence and research and, and give you an answer. But then whenever it's a medical question that requires care, that will be escalated up to a product called Lotus Cortex, which is a physician-facing sort of tool, which I can talk about now if you'd like.

There's kind of two different levels. One, an AI that is trained medical terminology, your health records, et cetera, that can directly and perhaps more quickly answer questions. And then, once you get into something that requires medical care like whatever, I've halfway chopped my arm off, then it's going to step up to some kind of a physician on the other end?

Yeah, yeah. And it doesn't have to be any, you know, that acute or drastic. It can be simple things as well. Anything that requires medical advice, prescription, you know, a lab order or a referral or just a second opinion, Lodus will escalate that up to a physician to review and, and give you advice on pretty quickly. And we can give you a— we can give you basically any prescription. We don't do controlled substances today, just to be, be safe in that area, because there's a lot of fraud and misuse there. But we can order labs for you, we can diagnose you, we can refer you to in-person care as well. So basically, all the sort of primitives of what you would imagine when you go visit a doctor in person, like a primary care doctor, we're able to do today. The only thing we can't do is we can't touch you and feel you because it's all virtual. But it turns out 70 to 80% of all Primary care visits can be done virtually. So, the amount of times a doctor needs to actually— it's pretty limited.

Until at-home palpating robots become a reality, which actually is a potential reality. Playing devil's advocate here, you mentioned that, for example, you might need to bring in the live, the human physician when, for example, prescription needs to be refilled. Is there a reason, legal or otherwise, that AI couldn't just see, okay, so they've been on this prescription for X period of time, they need a refill, they've requested a refill, here's what the most recent labs show in terms of whether they do or do not need that refill, and couldn't that process be entirely automated?

It is. Yeah. So, the physician is there to review what the AI is sort of coming up with and signing off on it. So all of that, I can talk a little bit about that if you want, where, what the physician experience is. So if you go to our website today, it's lotus.ai and click on Physician Cortex on the top as a physician, learn more about how the platform works and sign up. You go through an application process. If we approve you, we obviously verify you have an NPI number, you're licensed in the state that you're going to be practicing in, and we give you access to this tool called Lotus Cortex. As a physician now, it's almost like this Iron Man suit basically that we've built for the physician. And so what they see is they see a request come in from, let's say Ben is using the product on the patient side and you have, let's say, you know, you have an infection going on and you know, you can't see a doctor for another 2 weeks and you can't take time off of work, you gotta take care of, you you know, your chickens. And now it's, you're talking to Lotus at 1:00 AM. Lotus is asking you all these follow-up questions, building all this rich context. What it then does, it creates a SOAP note, which is a standardized sort of clinical note that a doctor creates when you go and see them. And it references basically all of the conversation history that you've ever had with Lotus, but also all of your data and all the latest evidence. And it creates a request that pops up in a queue for the physician, and the physician picks up that request. And in that request, they see essentially suggested care actions. This is what Lotus Cortex now is suggesting to the physician that, hey, this is exactly based on all the knowledge, all the medical knowledge in the world, as well as Ben's entire health history. This is probably the best option for Ben in terms of a prescription or a treatment plan. And then the doctor does their own clinical reasoning and is able to make a decision pretty quickly. And then they can hit a button and then all the other care is executed, like your prescription sent to your pharmacy, a lab is ordered, you get a response that gives you a very detailed breakdown on what to do next. All of that, by the way, on the doctor side happens within about 2 minutes. So we've effectively, what we've done is we've brought the, We've increased the productivity of the doctor by a factor of 10 because in a traditional primary care visit, it's about a 20-minute visit where most of the times the doctor's not even talking to you. They're looking at their computer trying to figure out what's going on. You know, you walk out of there sometimes more confused than you walked in there with a, you know, $30, $40 copay and sometimes a much bigger bill when you get home from the insurance. And, uh, and half of the times it's not even a doctor, it's like a nurse or a PA because we don't actually have enough doctors in this country, right? And, and not to mention you waited like 2 months to go to this visit and you had all these questions, but they're like, no, no, we can only talk about one thing because that's how I can bill insurance. You got to follow up and come back for all the other stuff, right? So we've basically collapsed all of that down to a 2-minute interaction, but in that 2-minute interaction from the physician they're not just treating one thing, they're able to treat you holistically because they can see all of your data at their fingertips. So, they have this sort of like Iron Man suit superpower that they're able to sort of practice at the top of their license. And that's how we've been able to bring the cost of delivering that care down significantly. Ben Greenfield: Okay.

Is a potential, I guess what some might consider to be a downside or a trade-off, the fact that you don't have a dedicated physician like whatever, your person, Carrie or Bob or whatever who you're used to seeing on a regular basis who you know their face and know their name? Are you kind of working through a series of physicians based on whoever happens to be available when you're logged into Lotus?

Absolutely. That's a great point actually. And sadly, the reality today is most people don't have that anyways, right? Most people change insurance every couple of years because most people change their job every couple of years. When you change your job, you change your insurance, you change your providers and your health systems that you typically work with. And even today, like I said, most of the times you're not seeing the physician, you're seeing a PA or a nurse. And they don't actually— like medicine used to be like before the emergence of sort of modern healthcare in America, there was sort of this concept of a general practitioner that would know you for your entire life almost and basically treat everything. Right. They even did things like surgeries. And then, then the sort of emergence of specialty care and big hospital systems came about where practice of medicine went from, you know, a physician, a family medicine doctor that you would call your family doctor, that your whole family would go to and understand your entire family and your life basically of what's going on. That was great. That doesn't exist anymore largely in this country because medicine has sort of been sort of moved into this big health systems that have consolidated and private equity owned sort of systems just acquiring up these small practices and these small sort of health hospitals have now sort of moved to specialty care, right? That's why like today a family medicine practice or even a family medicine doctor is actually one of the least paid doctors, right? Versus internal medicine specialty like surgeons, et cetera, because that's where all the money is. That's where hospitals make all the money, right? So, so the prestige of becoming a doctor has now moved into, uh, these sort of like specialties. So that stripped away the capabilities of a general practitioner, you know, or a general primary care physician to be able to do all these things now, to just do very surface level things and become almost like this triage agent for the rest of the specialty care system, right? When in reality that was the best way to deliver care to a community because that physician was deeply embedded in the community. They understood the community, they understood the family, what was going on, you know, over the course of your life. But that doesn't really exist today. And that's actually what we're building with Lotus. And the way to build that is have a single point of contact, which is Lotus AI, that remembers all the context of your life and is able to surface that up to whatever physician is treating you at that time. And give them all the latest context, and they can see a timeline of all the treatments that have been happening through Lotus outside of those. So you still— you actually get that relationship that, that where you're talking about, but it's not just with one doctor, it's, it's with a group of doctors, but largely it's with AI. It's with, it's with Lotus AI.

And that's which is technically more convenient. Lotus is the version of the country doctor with the leather satchel who shows up at your door. And in our day and age, that might be annoying because you know that what you really need is to step up to XYZ specialist. So, Lotus is kind of bridging that. And, I guess—

I would actually push back on that. One thing I would add is what Lotus is doing now to these doctors that we have on the platform is it's upskilling them to practice in areas of medicine that they may not be actually comfortable because normally they don't have all the context of the patient and they don't have this Iron Man suit available to ask a question that can go and reference all the latest medical literature and evidence to be able to answer a question. So for example—

oh, so, so, so by the— are you saying that it's turning the country doctor into more of a specialist then?

It's, it's allowing, uh, the country doctor now to practice all specialties because they have the— they have all the information at their fingertips, right? And the reason why specialties exist in medicine is that it's an artifact of the human mind, the limitation of the human mind, right? There's no way a doctor can keep up with all this information, every single specialty. But now with the AI at their fingertips, they're comfortable practicing cardiology, they're comfortable practicing a lot of these things that, you know, normally they wouldn't be able to really do. And so now we can get a lot more care upfront. So they only go to the cardiologist when they need, you know, interventional cardiologist when they need to get a stent put in, right? Or when they need the actual surgery and need to be under the, you know, under the knife. So that's really like moving healthcare from the sort of disjointed specialty care system, which charges you, you know, a whole lot of money every time you interact with it to bringing all of that care upfront. So that's kind of where we say we're upskilling the average doctor to be able to be comfortable The amount of medical knowledge that exists doubles every 7 months in the world. So, there's no way a human is able to keep up with that.

Yeah, such a pattern interrupt. It kind of makes you wonder if, you know, as models like this progress, how it might potentially change the face of medical education, you know, in terms of, you know, do you specialize or since that knowledge is already going to be at your fingertips, you know, do you go down a different path?

Exactly. And, We've actually come up with a name for it. It's called Infinite Care, and it's infinitely available, infinite capacity. And it, you know, it's really being able to give the doctors the, the, the confidence to practice in all areas of medicine where they can do it virtually, and giving the patient sort of instant access essentially to, to, to healthcare. Care in general. And, and by the way, like I said, all the healthcare is today sick care, reactive. What we're really doing here is preventative care and proactive care, right? So it's before you get sick, right? Things like, how do I optimize, you know, my diet, my fitness, right? And I, with these set of conditions that I have, right? And then from time to time I have an issue, I need a prescription, I can escalate up and get that, right? So there's there's a lot to be said about the future of medicine. I'm going to spend another hour on that rabbit hole, but I think it's a fascinating time.

Ben Greenfield: Yeah, elephant in the room, I guess, because I noticed this on the website unless I'm mistaken somehow from the physician side, from the monetization side, it appears to be largely free. So, if I'm a doctor and I sign up for this platform, how am I getting paid for actual patient care or time spent working.

Yeah. So we pay the doctor, right? And essentially the waste pays for the patient, right? And the doctors for that request that I was explaining to you that gets escalated when they resolve that, every time they provide treatment and they resolve a request, they're earning income and we're basically paying the doctor out. At the end of the month. So that's how the doctor gets paid. Now, on the patient side, it's actually completely free. So this is the other sort of radical part about what we're doing is flipping sort of the business model of healthcare upside down and happy to go into that rabbit hole as well.

Ben Greenfield: I would love to go into that because obviously I'm sure if I'm wondering it, other people are too. It's like, well, where the heck is the money coming from if it's free for the patient and then the doctor's getting paid?

Yeah, all of, all of, most of American healthcare today is what's called fee-for-service, right? So you go and you get something done, you get a service, rather, whether it's a procedure or, you know, advice or etc., and you typically pay a copay and then the insurance will pay the rest if you're insured, right? There's a small percentage of the population that's not insured, and then there's a small percentage that sees Concierge Medicine doctors, right, that pay tens of thousands of dollars a year and they're paying out of pocket. But largely American medicine today, American healthcare is, is a fee-for-service insurance reimbursed model. What that does is it actually incentivizes the doctor to see a large volume of patients so they can bill insurance as much as, you know, possible, and then incentivizes the insurance companies to deny a lot of these billings, which are called claims, and, uh, try to reduce, you know, the amount of care a patient is consuming from the system, right? So the system incentivizes the patient to actually not seek that much care, right? Because the more care they seek, the more the insurance companies have to pay, right? And so that's where they invent things like prior authorizations, for example, right, to try to slow down that load a little bit, right? The problem with all of that is it, it leaves the patient delaying care, right? That's the big problem in this country today is like, like you were mentioning, you have a bunch of dashboards that you have all your data on, right? It's great to get some insights, but if you need a prescription, you know, and if you need a medical diagnosis, if you need someone to actually order their lab for you and, and, and you know, you need to interact with the healthcare system, right?

Right. So, so, so if I, for example, have imbalanced lipids, let's say high ApoB and Lp, high particle count combined with some kind of like hemoglobin A1c and glucose elevation, kind of like the warning signs that I could be at risk from a cardiovascular standpoint. And it would actually be very prudent for me, let's say for me being able to be around for my family, I'm a male, I'm 50, to get like a CT angiography or or something like that, the 6 or 8 or 12-plus-month process of waiting could actually be a long enough period of time for something like plaque rupture to occur. So, in many cases, we're talking about a life or death or significant hospitalization issue if you're simply waiting and waiting and waiting after the warning signs have already been presented.

That's right, yeah. I mean, and you're a very knowledgeable person about your health and you understand maybe why that's a risk, but the average person doesn't, right? And what ends up happening is people have one or two bad experiences with healthcare where they'll wait 3 months to see a doctor and end up with a fat bill at the end of it. And next time they get sick, they're just like, you know what, I'm just gonna go to Google or ChatGPT and figure this out. And I don't want to take time off of work and drive an hour and like, you know, sit there for 30 minutes to talk to a doctor for 10 minutes, right? So The problem with that is because it's a very reactive system, people just delay care. And because people are delaying care, Americans are getting sicker and sicker. And then they— then what ends up happening is they get really sick and they end up in the emergency room, right? That's why we have a flood of patients always. That's why the emergency wait rooms are, you know, the wait times are so high because the hospital systems now from an ER perspective are overwhelmed., right? And, but then oftentimes, a lot of times it's too late for that patient by the time they get to the ER, right? And that's really where it's a reactive system versus what Lotus is doing is saying, hey, no, we're incentive, we're not charging you for this. Like, come consume care. Like, come use it every day if you want, right? We have patients that are— the average patient today on Lotus uses Lotus about 2 to 3 times a day. And because it's not always about I'm sick, it's, it's about how do I You know, how do I think about this? I have this health question, or I'm trying to optimize this, right? And all of that, by the way, is context that's continuously building on understanding you as a patient. So when you do get sick, when you need the care, we're able to give it to you like much faster, but give you like a way better personalized, you know, level of care. And so by keeping it free, we actually incentivize utilization, which is the exact opposite of what most of healthcare works like today. And by doing that, we increased engagement on the system and the product. And what ends up happening is it starts to then look like a consumer internet company, right? Like an Instagram, you know, or a WhatsApp or even Google, right? And so that's kind of how we're building it where we're building it as a consumer app, essentially, that people use on a very frequent basis.

But Google monetizes with ads. Free usually means somebody is paying somewhere. So, what's the vision there as far as how you're going to keep the lights on, so to speak?

Yeah. So, it basically turns out that if you can actually solve the problem where we're spending $5 trillion in this country today in healthcare, 100 million Americans don't have a primary care doctor still, and we spend a trillion dollars on billing, prior auths, admin sort of layer stuff. If you can do a couple of things, which we're doing all of these by the way, which are make the doctor 10 times more productive, collapse the cost of care. We're doing that by a factor of 60x today, and I could talk about the math there. And then it turns out you can actually help the patient and you improve the outcome. There's, there's like almost an infinite number of ways to make money, right? You think about things like, you know, CMS and Medicare today inventing things like the access program, right, where they're giving companies hundreds of dollars upfront to take care of a patient over the course of a year, let's say, right? And no one's really able to do that because all the healthcare system is set up for reactive care. We're really well positioned to take advantage of that actually, but we're completely free for the patient. So we're not participating in that right now, but we're very close to the HHS team and the CMS team. They're doing great work over there, but Ultimately, there's— I can give you a quick couple of quick ideas on how this makes money very long term, right? One is you think about— you brought up ads, right? And not all ads are bad. I mean, we see pharma ads on the TV all the time, and half the times, or actually 90% of the time, it's not relevant to you. But even outside of pharma, fitness and wellness is a $2 trillion market. So from a very safe, ethical advertising perspective, there's a massive advertising opportunity there. But then the second is really, you look at these large employers across the country that have, you know, thousands of employees, and they actually are their own insurance providers, right? So they're actually deeply invested in, in trying to keep their employees healthy because they can come to work and produce for the company, right? But these employees are often frustrated because they're, they're out there trying to seek care and they can't do it. So you could imagine an employer partnering with us and paying us $50 a pay, you know, an employee to give them unlimited sort of care, right?

Right. Shifting that spend from company insurance to using something like Lotus.

Exactly. It becomes an employee benefit, right? And then, by the way, the third, you know, and there's infinite number of ways to make money, but I'll give you the third one, which is health systems actually today, like I said earlier, make most of their money from specialty care, right? So for them, primary care typically is a loss leader because They're, they need that because that's how they get referrals into their specialties, right? But you look at companies like One Medical or Oak Street, these are like direct-to-consumer primary care, you know, systems. One Medical was acquired by Amazon, but essentially 30 or 40% of One Medical's revenue actually comes from triaging sick patients into health system specialty care, right? But they can do it better than the systems can because they can actually give these patients longitudinal like care. So you can imagine Lotus is actually able to do that even better because we deeply understand the patient, we understand where they live, what the problems are that they have, and then we actually can only— we can wait to refer them in when they absolutely need to go in. So it's better for the patient and it's better for the health system because the health system doesn't— they actually have a term for this, it's called a good patient versus a bad patient. A good patient is someone that actually needs that procedure and they can build insurance and maximize that time of the physician. A bad patient is someone that was worried and they came in and they don't actually need that procedure. Now they just wasted, you know, a 30 to 60 minute visit with the interventional cardiologist, for example, right? So it actually ends up cutting a lot of that waste because now when you go to a cardi— like, for example, the, the situation you were talking about, let's say you had a cardiovascular disease that you weren't aware of when— if you went to a cardiologist today, the first thing they're going to have you do is they're going to get you to get the lab test done and the lab work done. And now you just waited 6 months to see that cardiologist, and now they're like, hey, follow up, go get all these tests done, come back. On Lotus, we are able to do that upfront for the patient, and we're able to synthesize their entire medical history. So we only send you to the cardiologist when you actually are going to make that interaction useful for you and for them.. So you can imagine why health systems would be super interested in partnering with us for that. So we're, we're having some of those conversations as well, but we're really just invested in doing what's right for the patient today and building the best possible experience. And then also for the physicians, right? And, and we think once we get to scale, uh, there's an infinite number of ways to make money.

Yeah, I, I know it sounds like, and I wasn't quite sure when we started the interview, but it, it sounds like you actually do have patients using it right now. Patients and physicians using it right now, correct?

Yeah, we got patients.

Okay.

That allows me then to ask a few kind of like I guess data or evidence-based questions. And I don't know if you know the answers to these, but I'll throw a few at you. One thing that I see happen a lot in the health world, and I'm not a doctor, I do wind up talking with a lot of people about health issues though, spend several hours per week doing that. And people a lot of times have things like mystery symptoms, mystery clustered symptoms, things that have just been very hard to decipher and interpret, and they've been through the wringer of the medical system and have never been able to fully put the pieces together in terms of what their diagnosis or what their problem actually is. Have you had any patients like that go through Lotus and actually, get the answer based on kind of the complex interweb of AI and physicians that you've described?

Yeah, every day. Yeah, we have new success stories every day that are incredible and really gets us out of bed every day because we know that we're making an impact on people's lives. And there's a bunch on our website you can check out as well. There's a patient called Tricia that had pulsatile tinnitus for years and bounced around.

That's like the, uh, like the ringing in the ears, right?

Yeah, that's right. It's a ringing in the ears, and she had no answers. And, and it wasn't until Lotus can go and sort of unify all her records and cross-reference those symptoms and triggers and patterns with medical literature that she was able to start thinking about how to treat herself. Actually, she was able to like start getting relief the next day because she was able to start to understand her health conditions better. It's not that, you know, a lot of the, a lot of the times it's not that a doctor wouldn't know what to do. It's just the doctor doesn't have time to sit there and talk to Tricia for, you know, 5 hours and understand everything that's going on and know the latest, you know, research and evidence and literature that talks about these issues, right? And, or even have all that information unified in one, you know, in one place for them to look at, right? You know, you're having— we're having patients, for example, you know, you, you talk about, you know, I know a lot of your community is into biohacking and, you know, into really optimizing health. And we had a patient that was wearing an Oura Ring for the last year, and their resting heart rate was continuing to go up month over month while they were sleeping. And, uh, they were going to the doctors trying to figure out what was going on and then they never really brought that up because that wasn't something that they thought would be correlated with anything. And they told the doctor that they were having night sweats. And so the doctor put that in the EHR in the clinical note. Lotus was able to pick up that and a few other things in the person's medical history and be like, hey, you might have sleep apnea actually based on, you know, all this data that I'm looking at. And the patient ended up, you know, getting diagnosed for sleep apnea. And now he's on a CPAP and sleeping better. But that's just a simple example recently where it's not that the doctor wouldn't have known that, it's just they didn't have all that data in one place to be able to look at it and they weren't incentivized to actually do that and they didn't have the time to spend with the patient.

Right. And you make a good point too. I think with big data, you could actually in the long run, and again, I don't want this to be medical advice, again, I'm not a doctor, but you could eventually negate the need for more expensive protocols as AI is able to do a little bit better job as a predictive algorithm using what I think the best terminology would be proxies. Meaning maybe you haven't done a polysomnography or sleep lab test using your example, but we've noted that this cluster of symptoms, let's say night sweats, elevated body temperature, a certain impairment or imbalance in sleep architecture on an Oura or a Whoop or an Eight Sleep is correlated high enough with sleep apnea to where we can definitively say that you have it and you're skipping the sleep lab study and going straight into the solution. Or even with my example, right? We've identified based on treating whatever, let's say like a quarter million patients, that this cluster of symptoms correlates with X percentage unstable plaque in the arteries. Well, maybe we're going to save money in the medical system and just skip the CT angiography and go straight into, you know, whatever, retatrutide or PCSK9 or something like that. I think once you have enough data, you could make predictive algorithms like that that save the medical system money.

Yeah, that's exactly what I was referring to earlier around cutting out a lot of the waste and being able to unify the data. And there's a tremendous research opportunity. So we're doing a lot of research right now. We're publishing stuff as well, both on the clinical side as well on the applied AI side where we're looking at this data and finding very like incredible insights and And I think that our long-term vision really with that is to solve human health and be able to, you know, you can imagine over hundreds of millions of patients, subtyping that data, understanding really, you know, how a disease is developed, how it progresses, how to treat it, what treatments have worked, you know, for people that are similar to you or have had similar symptoms. There's a tremendous opportunity there, even from a research perspective and drug discovery perspective. So from a monetization perspective, that's not really something we worry about today. And, and you know, that's another, another idea there. But, but I think ultimately, like, it's really how do we build outside of the system in a way that patients trust us, they use us, and we're able to really be in their corner and give them agency over their health today. That then feeds into this really rich data set that we're building, um, right? And, and people are, you know, like you talked about it earlier too, it's like if you're if you're free, like, you're the product, right? And I think that, like, that's, that's one thing that we deeply believe in, is like the patient owns their data. Like, we're never going to sell data without your consent. Like, if you want us to share it with someone, we'll absolutely share it. But, right, I think that, like, ultimately the funny thing and the irony of it all actually is hospitals are actually already selling your data. And most people don't know that, right?

Who are they selling it to?

Oh, there's others, and they're selling it to pharma. They're selling into, uh, there's these big sort of like resellers of health data that they sort of do research on to try to figure out like, you know, how to optimize billing or how to optimize, you know, operations within a hospital. They, they, you know, they claim to de-identify it, and, and, but, but at the end of the day, it's your data that's being sold. That, yeah, most people People don't even know that's happening. Every single hospital in the country is doing that today.

So, so what you're saying is even if it's anonymized, it's being turned into a profit and the patient certainly isn't profiting from that.

Exactly. So the way we think about it is like if we can be transparent about it and, and actually advance science and help patients, especially patients that might have a rare disease that, you know, they don't know how to treat or solve in isolation. But if we can see that across millions of patients, there's an opportunity to help all of them, right? And get the patient's consent and be transparent about it. I actually, that's my hot take, which is like, I actually think people will be willing to sell their data if they get incredible care and incredible value from a product. Right. But that's not even something that's on our roadmap today. But to your point, you brought the idea up, which is there's a massive opportunity on the data side of it in terms of researching and subtyping and being able to garner insights at scale and really like it's called population health.

Right. You know, Back to prescriptions, I think that some people don't realize the number of prescription errors or inaccurate prescriptions or redundant prescriptions that result in damage to the patient. With Lotus, is the potential for there to be less of that occurring in terms of how the funnel goes from patient to physician to diagnosis to prescription to refill?

Yeah, absolutely. Like I said earlier, Lotus does a lot of the analysis that the physician typically doesn't have time to do. And it does, so we've trained on not only on the latest evidence in terms of research that's coming out, but also all the clinical guidelines that get published across the hundreds of specialties of medicine, right? Like American College of Physicians, American College of Cardiology, or American Heart Association published the, the standards of care and guidelines for each specialty. And so we've ingested that as well as the FDA drug database, as well as all the contraindications of prescriptions and medications. Lotus is able to reference all of that and do all of those cross-checks before the physician even looks at it. Right. So when they look at it, it's almost like the pharmacist says— so I don't know if you know this, but in hospital systems, typically a physician will write a prescription and then it'll go to a, a pharmacist in the hospital that will review that prescription to make sure it's not contraindicating with something else the patient's taking that maybe the physician didn't think of. Right, or it's the right dosage. And it'll go through like 2 or 3 manual checks before it gets sent to the pharmacy, before the patient gets that medication. But you can— that, that's, that's very inefficient, and there's actually human error that even happens within that, right? And, uh, like, I had a story recently. My, my sister was pregnant and she was at the hospital, and my nephew was in the NICU because he was born a little early. And the nurse, uh, and in the NICU, uh, gave the wrong medication to my nephew. And he was in the NICU, like one of the most intensive places to like monitor this stuff, because the doctor had prescribed the wrong medication. And, and, uh, because, you know, it must have been like a resident that was just tired at like 3 AM or something, you know. And then the pharmacist just like checked it off, and it went even through those like 2 or 3 layers. Like he ended up getting the wrong medication, and you know, luckily, like, you know, was, was able to recover from that, but they gave him an adult dosage, um, that would normally be never given to an infant. And so I think that, you know, it's, it's not that doctors are bad people, right? I think we, we think doctors are heroes, right? It's, it's really that the system that they're in today, they're burnt out because they're on this sort of conveyor belt of a volume game that they have to see at least 4 or 5 patients an hour in order to, like, turn a profit for the system, right? And so that, you know, at the end of the day, they're human, they burn out, you know, and they, they forget things sometimes, right? And, and, uh, that's really where Lotus is able to reduce a lot of those errors and, and improve care delivery, right?

Lotus, or AI in general, I guess, doesn't get sleep deprived. Occasional hallucinations, uh, which from what I understand, you guys have, have iron that out with your particular AI.

That's right. Yeah, that's what I was talking about, like the stuff we've trained on. So we've built— like, Lotus is grounded in all of that evidence and your data, as well as we have very strict guardrails on what it can and can't do. So all of that work reduces hallucinations, but also like error. And then, by the way, you know, sometimes the doctors disagree with Lodis. But that information is very valuable for us because every time they disagree, we're using that to do reinforcement learning on. And so the next time, the AI doesn't make that mistake. So it's getting better and better every time.

Right. So basically, as, as the system is used, it's being trained by both the doctors and the patients.

That's right.

Yeah. Quick question, uh, in terms of accessibility, is this Basically legal and accessible in all 50 states?

Yeah, it's legal and accessible. We just sort of slow rolling it out across the country right now because, because we, like I said, we have real physicians that we need to hire in each that are licensed in each of these states.

Right. I'm out in the middle of nowhere in North Idaho and I did download the app. It looked like I might have had a little bit of limited functionality, but that's not surprising based on my geography.

Yeah, we'll get some more physicians in Idaho that, you know, if they can handle the capacity, we'll open it up more and give you access. But that's sort of how— that's basically our biggest limiting factor. And that's growing fast as well because physicians are using Lotus Cortex and realizing that this is a better model of care, essentially, right? It's physician-led care at scale. But we're growing pretty fast across the country and hopefully we'll be larger in Idaho soon on the clinician capacity side. Cool.

Well, folks, you can find in the App Store, it's Lotus. I assume the physicians, should they go to the website or for physician also, is that the app?

No, Lotus AI on the App Store. And for physicians, it's just lotus.ai, the website. Then it'll take you to Cortex where you can sign up and actually start treating today if you're a physician, start making money.

Cool. And for those of you listening or watching, I'll link to all this if you go to bengreenfieldlife.com/lotus. That's bengreenfieldlife.com/lotus. Lotus. KJ, this is pretty cool. It's exciting to see developments in terms of the future of medicine that hopefully will make patients' lives easier, physicians' lives more convenient. So, thanks for doing this and thanks for doing what you're doing.

Thank you so much for having me.

Alright, folks. Thanks for watching. Have an incredible week.

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