Transcript
Speaker: The dirty secret of the whole mental health architecture is right now it is not considered as another healthcare problem. It means everything goes out of the pocket. Clinicians who is working with you is more interested interested to sell a engagement data. The dark truth of behavioral health is no one is incentivized to improve the outcome.
Speaker: This is the Home of Healthspan podcast, where we profile health and wellness role models, sharing their stories and the tools, practices, and routines they use to live a lively life.
Speaker: Deval Roy, welcome to the Home of Healthspan podcast. Pleasure. Pleasure is all mine. As a a former es escapee from McKinsey, I'm looking forward to our conversation in the health space.
Speaker: But before we jump into your story, everything you've been doing for the past decade, especially, but even before that, how would you describe yourself? I'm a ah lively founder and CEO of Holmosk.
Speaker: right I've spent a significant amount of time in the world of finance. I'm a student of economics and I'm very, very sort of keen to solve complex problems.
Speaker: Mental health is one of the largest ones that I am tackling. Yeah, I mean, the the human brain in general is the most complex problem that we have. And so I think Maybe from the outside, people might say, i i have a hard time understanding how how this finance guy, this management consultant, this economics focus now getting into the world of neuroscience and brain health. And so maybe before we jump into what you're doing, explain how that came together and why it's such a perfect natural fit.
Speaker: The single common point is data. okay When I started reading economics history, complex systems and system at a large is ah where is my intuition goes and where is my knowledge and the training goes.
Speaker: So economics is a system of, you know, economics is studying a full economy as a system, complex system, and all the variables that is moving into that. and And then I really spent solid 18 plus years. If there is a single thing that I learned from the world of economics and especially finance and trading,
Speaker: was how opinions don't matter. What matters is data. And that has been used in multiple sectors. I would say in know the world of you know the finance and economy is probably the biggest factor. It is used in defense. But you know I started looking at what are the application layer that goes beyond that.
Speaker: And that is where when I started looking health care, I thought that health care decision-making is highly dependent on opinions and highly dependent on multiple heuristics, which are important, but then not enough the moment you want to go to a very definitive and complex problems like mental health.
Speaker: So when I enter into this space, I was looking at what are the five major disease areas that truly influence any health system, okay or any country for that matter. And you find that in all of that,
Speaker: Chronic disease stands out the most, but you remove the mortality and say, okay, what is the factor that is truly impacting as a comorbid layer? And mental health stands out as the most complex and the most biggest layer that is out there if it has been completely untapped.
Speaker: And then look at mental health, okay, how is the decision making is being done in that? Everything is subjective. So the question comes down to it is, okay, how do I take all of these complexities and build the utility architecture of data on which the decisions should be made from a scientific point of view, from a rigorous point of view?
Speaker: And that is where the origin of almost comes in. a very geeky way to think through all of this, but that is, know, that is a connective tissue across economics, finance, and healthcare and what I'm trying to do.
Speaker: It makes a lot of sense. I'd love to understand a little more the difference with mental health and mental health care versus maybe some other modalities. So if you think about, we went way back in medical care and science, right? You had vapors and the ether and like, there's no data or science. And then We learn germ theory. We learn about viruses. And so it got more data driven. We have RCTs.
Speaker: We do that. And there is some of that with medications and treatments in the world of mental health. nothing a decent proportion still talk therapy, which is highly variable, I would imagine, based on who you're talking to.
Speaker: And then the other side, we have, we're getting more and more data with things like psychedelic treatments, but even with the data, it's not changing how we do care because of legal constraints or perceptions.
Speaker: And so why is mental health treated differently than maybe a broken bone or... a more physical sickness. And i would say this is where why it is that way that goes into the history of the problem.
Speaker: and know For whatever reasons, it was you know it was considered as a stepchild system. And we have either ignored it completely or have not truly identified as a healthcare problem, whereas it has become obvious now that it is a serious healthcare problem.
Speaker: I'll give you an analogy. When it comes to a stigma and that access issue, polio is still considered highly, know, a stigma issue. Or even when HIV came into the picture in the late ninety s you know, it was highly a stigma issue that it happens to a subset of the population. It doesn't happen to everyone.
Speaker: Okay. the amount of science that got built was very, very unique. If you look at even oncology, pediatric oncology in 1973, the death rate was 100%. Right now, the death rate is less than 1%. The progress of science happens or is built on the mountain of serious scientific data that has been put together.
Speaker: Why it has not been done in healthcare, that baffles me even today. But me as an e economist should not be building this company. This should have been built by, you know, the thousands of psychiatrists and the technicians and everyone else. This should have been done much earlier. So it was baffling to me to really see that it doesn't take exist.
Speaker: And for me to spend around 11 years to put together this amount of truly built the scientific layer of this machine has been probably the biggest proud point and biggest achievement that I can think of.
Speaker: Yeah, and I'm excited to dive into that. I am curious a little bit, Ray, like we talk about health span on this show as opposed to just lifespan and sick care. It's more, how how can we live better? And in a system, especially in the U.S., s but most of Western medicine that is not a healthcare system, but a sick care system. of We see what goes wrong physically and we address it because we see the downside, but we we don't seem to play a lot for the upside of, hey, you may be operating at 60%.
Speaker: And with these minor changes, we could get you to 85. And here's what the impact on the economy could be if everybody was operating at 85 instead of 60. And I would imagine the same is true for mental healthcare of, We could get clinical depression and suicidality and have real issues, or we could have malaise that's still taking away 20% from productivity that we would still address.
Speaker: Absolutely. Let me articulate that point exactly. I think both of us are talking to the same layer. Number one, is if you look at the current healthcare, it's all around s sick care, it's not preventive at all.
Speaker: The language that you are speaking is the emerging field of longevity and longevity of the health span, all of these is literally all around how do we make it preventive healthcare. But if you really even slice that, every component of longevity right now is focused on metabolic, all on the body, not on the mind, in the brain.
Speaker: Okay? and the healthcare of the future will demand that, okay, how do I really you know compress that into a single entity so that we can really think around mental and physical health in a very serious way. So neuro longevity is an extremely large emerging field that, okay, how do I really take care of the brain so that you know the body you know becomes important?
Speaker: okay And the the brain component is... a very high component that has been completely untouched and tapped. Okay. But for me, i mean, the intent was that, okay, how do I bring mental health or neuroscience or neuro in general, you know, at par with say oncology or cardiology or any other metabolic disease? Because right now it is not at par.
Speaker: Okay. Okay. And I guess as you're building this data set, right, and right you you kind of it's a luck is when preparation meet meets opportunity, right? And so it's, you're kind of 11 years in when COVID hits and people think about mental health in a new way. Once AI starts taking off in a new way and people trust in different ways for medical care. And so these trends, meta trends are converging at the right time for Hall Musk.
Speaker: What are the things that you're looking at to say this works and this doesn't given the, so much, say, is talk therapy. And so it's very hard to disaggregate what happened in this talk therapy session versus this and with this person versus that one. 100%. I mean, you are asking a fundamental question that what is the evidence of effectiveness, whether it is talk or whether it is medicine, okay? And unless that we ask that question rigorously, you know, and backtest it in thousands different ways,
Speaker: We will never have, you know, I like to say that mental health is right now closer to poetry than science. You know, you'll have more poetry, you will not have science. Okay? I would even like to say that the the reality is that the amount of learning that we have learned by building this database and the observational facts around it is a full proof that it can be really fundamentally changed. So when you talk about the tailwind, the two tailwind that has been, or the mega trend that has been in our favor is he started this company in 2015.
Speaker: At that time, mental health was not sexy. And building this up to the 2020, when the COVID it happened, the need was obvious even before that. okay We all know that how BH is one of the most complex areas. And it is one of the largest comorbid areas. But no one had spent.
Speaker: Comorbidity made it to common and you know acceptable to speak about it in a very open fashion. okay But the larger trend right now is all... AI, okay, five years or 10 years from now, every solution matrix will have AI layer, or all major solution layer will have AI layer. And in that AI layer, the key differentiating agent is going to be data.
Speaker: Okay, so if I look at it, certainly that these two tailwinds are in favor of Hall-Mask in terms of really bringing this problem to the limelight and solving it in a very legitimate fashion.
Speaker: So what does that data set look like? Having spent 11 years pull pulling it together so far... So the foundational layer of all of that is all clinical data. So what is a patient behavior? the Literally the symptoms of a patient, what the patient has gone through, why the clinicians have made the decision, and what they have made, right or wrong, okay? And what has been the effect of that decision making?
Speaker: how much do we know? ahll Let me simplify that. We have approximately around 5 trillion tokens for around 41 million patients. let Let me bring it down.
Speaker: We almost like for each patient, we almost like have a book. And for some of the high density patient, we almost have an encyclopedia. This didn't exist before.
Speaker: What can you build on the back of this? What are the problems that you can solve? Is the you know the future to decide? And that's where the future comes down to. And so, sorry to just keep pushing here, but I'm just very curious on there's say you have blood pressure, right? there's There's an objective metric. And so I can say, hey, this pill, this person got versus this placebo versus pill. And I see it before and I see two months later and I see two years later, right? Like I have these objective measures.
Speaker: And what I'd be curious about are what are the kinds of interventions and what are the objective measures that you're able to see in this sort of data set? Because mental health is a different thing than just like cra tracking data on a a brand. pander It will be extremely aggressive for me to make those, here are the biomarkers that you know should track. okay Frankly, that would be the domain of clinicians and the scientists.
Speaker: okay My data set is a way to bring out you know sort of a foundation by which each one of these guys can query and ask. Like we have thousands of measures, thousands of measures, almost like millions of measures. okay Which subset of the measures are the most relevant? I think the APAs of the world or psychiatry communities of the world or neuroscientists of the world should be the definitive qualitative metrics around it. If I say it, will be too aggressive and too self-serving. okay But what I can say without making any challenge is measure.
Speaker: Whatever you want to measure, measure. If you're not measuring it, okay or measure at least a subset of it. okay Equivalent of a temperature or equivalent of HbOc or biomarker doesn't take exist. The endpoints don't exist in behavioral health.
Speaker: But only because the endpoints doesn't exist, that does not mean can't measure pH Q9, you can't measure GAT score, or you can't measure outcome score, or you can't even have a subjective view of whether you are feeling better or not.
Speaker: Find the matrix that works for you. Measuring it is, that itself changes the outcome because then you can see it longitudinally. That's number one. Number two, trial and error.
Speaker: The amount of trial and error that goes into it is almost infinite right now. By looking at this much of data, I can reduce the trial and error by 30% to 50%.
Speaker: And that itself is a mega gain. Mega gain at a patient level, mega gain at a system level, mega gain at a country level, and all of that. So these are some of the very basic things that is almost like a ground truth that I can make it without any challenge.
Speaker: And then is it, so is HOMAS platform more for practitioners or? Yeah, everyone. Everyone. So it's a practice is certainly the goalpost. So the simple theory is how do I learn from each patient to make the next patient better?
Speaker: Okay. And if you extrapolate that larger the number, the the volume and the density of the data makes it very powerful. Whether you want to make a patient individual level better, whether you want to make the patient at a cohort level better, or whether you want to do R&D, whether you want to do drug development, whether you want to do you know very concerted cohort analysis, whether you want to do FDA submission, all scientific publications, academic publications, all of that can be done on the back of the data.
Speaker: And so from a patient's perspective, is it you you intake and then you're categorized of like, here's the issue you're having. And then here's, whether it's a survey or something, here's where you're scored on that issue at intake.
Speaker: Then we know what the intervention and timeline was like, oh, you're going to do a DBT group and three times a week for the next three months. And then we can kind of see after one month, after two months, after three months, what it looks like after 12 months, what does it look like? That's how it kind of works to build that data.
Speaker: So for me, the idea is I'll give you an analogy. I'm utility architecture in the sense that this is what I'm building is ah how do I collect all the different kind of data, curate them and put them into an architecture and a platform which can be queried.
Speaker: What's the use case? Whether it is DBT, whether it is therapy, whether it is different, that is a function of the stakeholders who wants to extract the meaning out of it. Okay? My job is to really build the largest utility layer of the data and put the highest quantity of the data, the volume of the data, the quality of the data, scientific layer of the data, intelligence of the data, so that that utility layer is what can be used by the industry.
Speaker: Okay. For sure. It just seems like there's a tension in this world. So you go get blood work. Yes. They take X number of vials. And so you're getting pages of objective, hear the numbers, right? And it's no extra work. Once a person's sitting in the chair giving the blood, it's not a bunch of extra work.
Speaker: Whereas a lot of times with mental health, It surveys, it's questionnaires, like to get more and more data, you have to ask more and more questions, which may dilute the adherence. So you have to balance this tension of so trying to get your data. But I also want to make sure I'm getting everybody to completion so that I can get everything. Yeah. Listen, I mean, you can have multiple different technology. You can have ambient technology. You can have, you know, the voice, you can have video, you know, all of those. can be different extracting mechanism. you know If you truly patient consent, so it's a function of you can even put all kind of devices through which you can. you know For me to understand and know really make it extremely meaningful is certainly the more the quality of the data that you can capture, the better it is.
Speaker: But for me, the subset or the foundational layer is all around the clinical data. The clinical component of the data is where it is important. It means what are you going through? What are the clinical symptoms of it?
Speaker: What medications you are having? What side effect do you are having? you know What's the lung maternity? Are you improving or not improving? So what's the outcome metrics around it? you know and is there a comorbid layer of what you are having with something else?
Speaker: So a typical diabetes patient would also have a depression element. A typical obesity patient also have depression element. so How do we really differentiate one versus the other? Most corporate wellness programs overwhelm employees with too much, or they offer too little to be useful.
Speaker: Alively gets it right. We start with each employee's data and goals, then serve one clear, personalized action at a time. No noise, just real behavioral change.
Speaker: The outcome? A healthier, happier workforce. A measurable impact for your business. Visit Alively.com to see personalized wellness in action. I'd be curious if there are ways to tie it to, it'd be correlations, but correlations that may make sense of, hey, let's track your sleep.
Speaker: hopefully Two months prior, if you had Oura Ring or a Whoop band or an Apple Watch, we can see, wow, you were only getting an average of an hour and 20 minutes of restorative sleep. And then now you put these treatments in, you're getting more than two hours combined.
Speaker: Or... look, if we're measuring hormone levels always at 8 a.m. at this point in your cycle, if you're a woman or at this point in the the month, if you're man, like we can see cortisol levels have changed or something.
Speaker: Are there things like that, that you can tie it more to objective data that people may be. You can. Yeah. ah We just haven't done those explicit studies, but I'm sure we can.
Speaker: Yeah. So how how did you get access to 41 million patients data? Like how did that? Hard work. the The very first half million we partnered with, you know, we came across a specialist EHR from Duke University called MindLink.
Speaker: ah We acquired that company and, you know, it had data for half a million patients and we started curating it. You know, the dirty part of all of this is the curation is extremely expensive.
Speaker: Okay. I really thought that it would take me around two years and maybe around $5 million dollars to do it. you know When we started curating it so that I can capture more data, it literally took me almost seven years and 70 million plus.
Speaker: Just for that half million? and No, the half million was the starting point. But by the time I could build a machine by which I can capture more data and kind of scale it, it was a massive, massive affair.
Speaker: And to do it in a way that not only curating was important, but also scientifically validate to the market. The validation layer is very important so that the clinicians can believe in this data. the The MD, PhDs of the pharma companies can believe. The academic scientists can believe.
Speaker: So that the usability and the validation layer, both from scientific point of view and the commercial point of view, is very important. So aggregation is the one component. Curation is the hardest component. And then validation layer. So the curation component comes all the logic architecture by which, you know, the intelligence of the logic architecture by which you want to keep the science, you know,
Speaker: you know compact and in such a way there is no spurious sort of behavior that is going on. All of that is very, very hard. And I guess 15 years on, you you said one of the promise and the the benefit when done right is each patient that comes in gets you even smarter and better for the next patient. And so are you seeing that happen with, hey, we had a 60% success rate with our clinical recommendations kind of in year eight, that it went up to 70% by year 12 are now at X percent by year 15. Are you able to see that over time?
Speaker: So the healthcare systems where we work with and you know they do see the analytics layer that they built on the top of it and how they use it, whether they use it for clinical purposes or whether they use it for What have you? Quality metrics perspective, resource utilization perspective, pretty much all of them see the ROI. We get to deep phenotyping questions to do with, say, Oxford University. but We get huge policy research questions to be answered by, say, Stanford of the world.
Speaker: We get every pharmaceutical company that, okay, we truly want to understand, say, is schizophrenia a patient. And so we get to deep phenotyping questions that pharma is very, very interested to understand so that they can build the indications of the drugs for that.
Speaker: ah All of those are the layer of questions that we have been answering in a very meaningful fashion. Yeah, I guess I'm asking a different question because it could be... if it was a coin flip before, to your point on trial and error, right?
Speaker: Before Hulmusk existed, it's a coin flip. And that's kind of where we've been for 50 years or more, right? Like it's may work, may not, and we don't know. And as soon as you had this data set and launched, you may have taken it to 60%. So anyone who rolls it out, they're already better off. It's 20% improvement, like amazing. impact Because what I'm asking is, over time, your whole point is, as the data set gets bigger, as I get more clients in, as I get more patients in, it's getting smarter. Are you able, not just, hey, once we roll out in the system, they see an improvement, but are you at a macro level saying, hey, we are improving with this engine over time?
Speaker: Not yet. ah So we as an engine are certainly improving. The pace at which we are curating the data and the pace at which we are increasing the density of the data is we are certainly improving. I'm asking on outcome. So I like you know, the question is on outcome. Yeah. We we haven't done that in those studies yet. Okay. We haven't done, and probably the system doesn't have the incentive to do those the studies.
Speaker: You know, so this is something that a system has to think around that, okay, if say in a state who is incentivized to do that study to drive the policy. Okay. The dirty secret of the whole mental health architecture is that Right now, it is not considered as another health care problem.
Speaker: and So there is zero parity for it. So reimbursement doesn't apply. It means everything goes out of the pocket. Out of the pocket, and our clinicians who are working with you are more interested to sell a engagement data. So yes, you know so it's the more all-around engagement.
Speaker: The dark truth of behavioral health is no one is incentivized to improve the outcome. Yeah, which is really, really sad. there's a whatset it's Super sad. There's a book that came out recently I read called Bad Therapy.
Speaker: of it And it it's a lot on this, on talk therapy and like pathologizing everything with children. And that everybody has a therapist, all these teenagers, and it's just, is it really worse or are we just making them think about it all the time?
Speaker: And as a business model, right, you would say, hey, therapists, like we know from data that The single best thing we could do for these kids' health is tell all parents, do not give them a smartphone.
Speaker: Do not let them on social media, right? Like, we'd fix all the problems. Let's just stop there. But instead, say, oh, no, they probably should come in to see me twice a week instead of just once a week, right? and And the incentives are completely misaligned for treatment versus engagement, like you said.
Speaker: Where do we go then? then That tells you how complex it is, okay? and wait till you haven't seen anything yet. If you thought that social media was complex, wait till you see AI.
Speaker: The virtual psychosis, the engagement layer is on a turbo. The engine is so much more, you know like literally it can steer you into a direction that you feel that you are part of the matrix.
Speaker: okay And that's the that's the tsunami that we are dealing with Yeah. I mean, not to be a techno optimist, but it can also be this powerful tool. Like I spent my weekends with my friends, kids teaching them like, let's use lovable to build the game you always wanted to play.
Speaker: Right. Instead of you paying someone else to play their game, let's build the game you want because it' e it go yeah. it How do you get these things to work for you? And I just worry, just like with therapy or any of this other, if we tell kids, oh, it's just so dangerous, oh, you're growing up in the worst time, right? They go in with this fear versus, oh my God, you live in the best time. Look at what you're able to do in the afternoon with all these skills, with your imagination. Then they live in this time of excitement and possibility and abundance. 100%. 100%. And I could not be...
Speaker: and i could not be more optimist about changing the sector because what it is possible now, it is accepted. The tools are here. The data is here.
Speaker: okay It's purely a function of ambition and how far we are to correct it in the coming years. And know I wonder if it it helps solve the problem. So if one of the things holding it back was, it is easy to tell if someone has a fever versus doesn't, right? Like we can go take their temperature and see.
Speaker: It is easy to tell if someone has a disease versus doesn't, you take blood work, right? Whereas, and this was a problem with physical pain, right? There's this whole question of, well, is your seven the same as my seven? like how much pain is there really? And this is where...
Speaker: the Sacklers and Purdue Pharma did this amazing job of lobbying and training everybody here. Here's how we need to think about pain and pain management. And wonder now with data sets, like almost with what you're doing, you can get more of these objective markers and say, no, no, no, no.
Speaker: This is not a question of is it a long string or a short string? It is, we can tell these objective measures of before and after this person is operating at 70%. Maybe they're not clinically depressed, but 70% is not where we want them in society. We want them at 90 or they are clinically depressed and we really need to jump in and it's an emergency.
Speaker: Do you think that's possible? It's possible, 100% possible. And in how how do we get there? A willing system which wants to prioritize mental health as a priority and you know and a payment model by which it can be reimbursed. So the key question is solving the evidence layer of the problem. What works?
Speaker: OK. We have the data We have the clinicians. The need is there. The payment model is going to be a function of evidence. Evidence is going to be a function of clinicians sticking by creating a set of standards that applies.
Speaker: And that will be derived from probably the best data set that is out there in world. Yeah, I mean, so I think back to the the ACA, right? Where instead of saying, hey, we're we're paying for the treatment, we're paying for results. hey if your If your patient is readmitted two weeks later, <unk> you're getting paid less. And CMS access, right? The thed administration now is going with, hey, you can get reimbursed for these things, but we're going to go measure clinical outcomes and that your level of reimbursement depends on did you drive these clinical outcomes that we care about? And so the more that we can get to that world,
Speaker: where it's hey We have objective metrics and I'm not paying you for the more sessions you go to, the more you get paid, but the more you get paid, the more you improve someone objectively.
Speaker: awesome and iben Yeah. We just need those measures.
Speaker: It'll come one day. Yeah, I don't, I don't look Naval, I don't know if you can wait for these other people to do it. Like you, you, your point of, Hey, it shouldn't be an economist doing this. It should be someone in this world. They didn't, it took you to do that.
Speaker: You're sitting on the data set. You have the data science team. It might be you that needs to come up with, Hey, here are the hallmarks that we're seeing that are the things that move. And look, look, We're putting it out some smart PhD student who needs to go do some research. Like, let's give you the data to go publish on this and and make the case that these should be the mental health markers.
Speaker: Listen, I mean, our goalpost is literally how do I bring this data set into as many hands as possible, okay? ah It would be extremely self-serving on my point to you know say that, okay, I have the utility and I have the insight both, okay?
Speaker: That is where you know the conflict of interest comes in. And I want to be very, but you know sort of extremely sacrosanct about that I don't want to be that okay. It's in my best interest to say that, okay, here are the measures. But the reality is...
Speaker: the scientific community or the clinical community have to really accept it. And there has to be a mechanism by which these things really get built and deployed. So I can come up with 10 different metrics that is really important and say that these are ones that matter.
Speaker: But the reality is the acceptance and the adoption and the mechanism by which the system works will be a burden of a lot of other institutions that have to really attract from this, you know, whether even FDA.
Speaker: Yeah, i would I would challenge it, Rayleigh, because maybe it's the scientists, but the people who are really spending money on it are federal government and insurers. And so if you go to an insurer and say, hey, I can show these are them, like, they're the ones with the deep pockets. they say Look, it's just like Moody's or S&P. You become not just a data source, but the scoring, like, we have our...
Speaker: HOMAS rating. Like, here's the HOMAS ratings. Here's the scorecard. Here's what it looks like. Yeah, my take was when I started building it, I mean, you know, might know a company called Bloomberg. You know, my my whole game was to build a Bloomberg for healthcare. That is what we have done. We have built the best Bloomberg for neuroscience or neurosecurity or the edge, whatever you want to call it.
Speaker: Well, you know, this is the one of those, the overnight success, a decade and a half in the making. Like, it's I know how many hours, how many years, how many sleepless nights go into pulling all this together, but I'm really excited for the possibilities it unlocks. Yeah, and that is a whole game. The idea was, you know, it has taken 11 years of my life, and a team of, not only my life, but approximately around 100 plus people, approximately around $105 million dollars deployed. we have The risk capital is very high.
Speaker: But the science that we have come up with, the metrics that we have come up with, this is the most unique, largest data set, which has been the missing layer in the whole of healthcare. We have brought that to light and have done it in a way where the science has been upheld. So it has been a massive intelligence layer and a massive validation layer, which is done by the scientific community, used very extensively and all of that.
Speaker: Those are certainly the fruits of you know where we are. My goalpost would be that it unlocks many things that can be done on the back of it.
Speaker: And the mechanism by which that can be unlocked, the future will decide. okay But the coming years are certainly extremely interesting for the field, for us, and for the sector. Yeah. and And for those who want to follow the journey more closely, where can they find more about you, more about Holmusk?
Speaker: Holmusk.com. Anything to do with Holmusk. Anyone can Google me nowadays. It's very easy to find. So, novel roy very easy. But I am 24-7 obsessed with this project. So I'm very, very interested in, I mean, certainly I'm very interested from the angle of Holmusk perspective, what it is.
Speaker: But the larger game plan is, okay, how do I bring it out to as many stakeholders as possible? And that's where the AIs of the world is going to play a big part. okay Because the their penetration and the speed at which that option will happen will be very, very fast.
Speaker: So you are not stuck with the stakeholders, politics of the stakeholders. But the largest component is the future of healthcare is going to be focused on healthspan and neuro is going to be a big knowledge of the healthspan.
Speaker: And I can't emphasize enough that okay knowing what we know, making neuro as the part of the healthspan discussion or the longevity discussion you know is literally, and it's like I was somewhere i was reading that I can literally build a blueprint for being alive.
Speaker: It's like aliveness is not only a function of your metabolic risk, but ah aliveness is a function of being metabolic plus neuro combined as a human being. OK? So the elements of aliveness, I can really build a full blueprint. Because the metabolic to a great extent exists in a thousand different ways. okay The neuro component is the missing link. If you mix mix that, you can truly build a blueprint for healthcare, which is extremely different, which can be a function of being alive rather than being sick.
Speaker: Yeah. True health care and health optimization. well It's a very exciting and promising field. And i'm I'm happy and grateful that you jumped into this void that nobody else was filling to start doing this.
Speaker: A decade before now, right? So that you plant the tree of the the shade you'll never sit under. So it's amazing it takes so long. So I appreciate it. It's people like you that go and do the hard work to to deliver it to the rest of us. So thank you so much. people Thank Andrew. Thank you for having me. It was a pleasure talking to you. And certainly it's extremely, extremely in interesting field and how it is emerging at the pace of the Thank you for joining us on today's episode of the Home of Healthspan podcast.
Speaker: And remember, you can always find the products, practices, and routines mentioned by today's guests, as well as many other Healthspan role models on Alively.com. Enjoy a lively day.
