This episode explores Kalibra, a new healthcare AI tool. It aggregates patient data from multiple sources and helps clinicians and patients alike prioritize health information based on functional and lifestyle medicine principles.

Our guest on this episode of the podcast, Ivan Vatchkov, the CEO of Kalibra, shares with us how Kalibra.ai performs diverse functions, including bringing together all patient data into one dynamic place. It digitizes, organizes and analyzes the many PDFs from health wearables and lab, gut microbiome, and functional medicine testing.

Listen to the full conversation to:

  • Understand how you can simplify your practice management
  • Consider how data collection and analysis can streamline healthcare, especially in conventional medicine
  • Discover how lifestyle medicine principles were built into Kalibra
  • Explore the potential of AI health coaches as powerful tools to help patients make lifestyle changes
  • And much, much more

The Evolution of Medicine has sought to be inspiring, present valuable clinical content, and highlight technology and practice management topics that advance lifestyle and functional medicine. Now that AI is becoming more prevalent, we are asking, “how will this impact the evolution of medicine?”

Kalibra is tangible, it is real, and it is exciting. This is the Evolution of Medicine. Enjoy.


Connecting Patient Data and Killing the PDF | Ep. 301


James Maskell: Hello and welcome to the podcast. This week we have something that is all the elements of the Evolution of Medicine. Throughout the Evolution of Medicine, we’ve tried to be inspiring. We’ve tried to bring amazing clinical content. We’ve tried to talk about practice management and technology and all the different bits that go into how will medicine evolve and adapt to this new environment. And I’m super excited to welcome a friend on the podcast today that I met for the first time in 2015. So the Functional Forum was just getting started, and I went to a wedding in Italy. I have a friend of mine from New York, and her cousin was a banker from Singapore who was also a biohacker. And we had a conversation there where I told him what I did, and he was fascinated about functional medicine and the functional forum and everything, but he said, “One day, I’m going to do something in this space.”

And I’m super excited today because he has started a new company called Kalibra. You can check out go evomed.com/kalibra, but it’s many things in one. You’ll hear about it tonight. One of the things that it does is bring together all of the patient data into one dynamic place. So, all the PDFs of lab testing and functional medicine testing and wearables and microbiome testing, all of that into one place. And then secondly, actually helps your patients know what to do to implement that based on the pillars of lifestyle medicine and whatever protocol you put on top of it. When you hear and when you understand the implications, one, I think you’ll see that you are running, your practice could be infinitesimally simpler, even dealing with a much more complicated dataset. And I think for many practitioners who are burnt out, tired, morally injured, this could be very enticing.

So, on the podcast today, you’re going to hear about how Kalibra is killing the PDF, which I think will be exciting to a lot of people listening. He’s going to be talking about essentially what Kalibra is, which is longevity as a service. And I’m super grateful to Ivan for being a man of his word, for actually building something awesome. And as you’ll hear, there’s even more reasons why I’m excited about it that you might expect when we get into talking about the AI virtual coach. So, I know there’s a lot of chat about AI these days and how you can use AI to improve your practice. This is tangible. This is real. This is exciting. This is the Evolution of Medicine. Enjoy.

Welcome to the Evolution of Medicine podcast, the place health professionals come to hear from innovators and agitators leading the charge. We cover the latest clinical breakthroughs in health technology, as well as practical tools to help you transform your practice and the health of your community. This podcast is brought to you by the Lifestyle Matrix Resource Center, who provide a range of options to help you deliver successful, effective, functional, and integrative medicine. To find out more and to get started, go to goevomed.com/lmrc. That’s goevomed.com/lmrc.

So, a warm welcome to the Evolution of Medicine podcast, Ivan Vatchkov. Welcome, Ivan. Pleasure to have you.

Ivan Vatchkov: Pleased to be here, James Maskell.

James Maskell: Yeah, so look, I really want to get into a topic that you and I have been talking here for a few years, and that is really talking about the state of data and the state of technology. And obviously in the functional medicine community, there’s an improvement over the baseline of medicine in that you’re taking a long history, you’re spending more time with people, you’re getting to know people. You may even be running other tests, but the baseline is pretty poor.

And I think that over the years, we’ve had some interesting conversations about this, and now you are working on a project to solve this. And s,o I guess maybe just take us back to the beginning of your thinking and how we got here.

Ivan Vatchkov: Absolutely. And thanks for the opportunity. Look, if I take the perspective of me as a client, I mostly interact with a healthcare system with a tool that was invented in the 1990s called PDF. And the problem with PDF documentation is that it’s a time snapshot in time, but it doesn’t really give you the context that you need to understand someone well.
And I think your point is very well taken that in functional medicine, we actually cast an eye back to history to understand the context in which this set of markers occurred. And we try to tell the story, paint a slightly more highly pixelated picture of health.

Now, doctors today are very pressed for time. So, they don’t really have the luxury, especially in the traditional medicine space, to go through that discovery process. I think the statistics are that they spend about seven or eight minutes with you. They interrupt you within the first minute of talking. So, it’s very difficult for somebody who is incredibly busy to take the time to craft the context, which is really what medicine is about. It’s the detail that makes you you, so that I can personalize my approach and understand you well.

And our approach has been to try to move away from PDFing the story of healthcare and into creating an operating system for living data that allows the practitioner to seamlessly and easily have a 360 degree view of their client. We call this continuous care. So, whether it’s something conventional these days, like reaching into their wearable and seeing how they sleep, or having something more advanced, which is a particular set of at-home testing tools that they can prompt their client to use. Or simply having an operating system to organize all of this data into something coherent and intuitive.

I think we are now pushing the limits of what the traditional structures can offer. And the problem with this is that the data about this is exploding. So, as a client, you and me would want our practitioner, whether it’s a functional medicine doctor, traditional doctor, or perhaps a health coach or nutritionist, to take all of the data about us into consideration when they’re making lifestyle suggestions.

And today that’s very, very difficult unless the practitioner has unlimited time. So, the way I’m sort of positioning and we’re positioning at Kalibra to solve this is to give the practitioner the 360 degree view while acknowledging that health is more than just physical health. It’s mental, social, emotional. How do you tie these things together when some of the data sets are objective data, like a lab or wearables, some is highly subjective, your self-reported wellbeing, and some sort of process, both like your mindful minutes? Well, that’s a digital marker, but in essence, it tells us a little bit about how you’re spending your time on reflection, which is a key component of health.

So, to tie this all together, the idea is to have an operating system for health that can process diverse data sets, not just medical records, not just wearables, but all the data that’s relevant about us, but serve as something to the practitioner that is coherent and intuitive rather than a big data dump.

So, to summarize it, we’re moving from actionable insight to actually something that the patient can understand in real time. Okay, this is what we’re actually doing about your health. This is the schedule or this is the set of actions. So, I think tying that together is the missing piece right now.

James Maskell: Absolutely. Yeah. I think we’ve been saying for a while, I mean even back in, I remember during 2017, we did a whole summit, virtual summit on interpreting your genetics. And I know at that point, because genetic testing had moved quicker in the consumer space than the practitioner space, you were having a lot of doctors that were having people showing up saying, “Here’s my genetic test, what shall I do?” And obviously it’s a ridiculous printout. It’s 50 pages long. The doctor doesn’t know really what to make of it. We don’t even know at this point whether those genetic markers should be base for treatment at all.

And the longer that goes along, we’re thinking, actually, you probably shouldn’t treat based on the SNPs or anything like that. It should be epigenetics or whatever. So, that was just I think the first example of practitioners getting way overwhelmed by data that they couldn’t sort even if they wanted to.

Now this new data comes around with wearables, and from my impression, there’s probably 10 clinics in the country where they charge enough to spend enough time looking for everything to do one sort of snapshot. But even then, the doctor is having to keep everything together and prioritize in a way that is an exponentially bigger data set than they’ve ever had to deal with. And who knows whether that’s being prioritized or organized in the right way.

And so, I’ve been kind of waiting to see, how is this going to get sorted out? Because ultimately all of this amazing new data is coming in, there’s no one really in healthcare trained to interpret it or do anything. And ultimately, there are some places that are teaching doctors to do this, but you have to, it’s such, it’s like the business model can never catch up to the amount of data that needs to be sorted, even if you could sort it in any meaningful way.

Ivan Vatchkov: No, I think you make a great point, which is if you’re a modern practitioner, it’s not unlike sort of putting your head in a gazer. Because yes, there’s 300 pages of genetic data, there’s all sorts of wearables, but the tapestry of the story isn’t changed.

And I think you make a great point. There’s about 10 clinics probably in the US that can handle this. But actually when you sit down with them across the table, the practitioner is still going to double click into PDFs and show you various report and try to tie the story themselves, which is incredibly inefficient because it puts a very natural limit to how many of these you can do per day. Maybe five, six, seven.

And actually I’m talking today to one of the most modern and incredibly expensive such programs in the U.S. And lo and behold, it’s still a PDF-based system, which is astonishing. So, I think your point is well taken, and I think the way to handle this is not to try to go trolling all the data for insight. Sort of if you take the allegory of a big trawler that sort of casts net at the bottom of the ocean, scrapes everything and tries to make sense of it. Instead, we should be spearfishing. We should be saying, “Okay, take the most important bits first and discard everything else, but tie it together into a coherent story.” So, if you take the genetics example, right? There was a gentleman that published a book called Dirty Genes that talks about the nine most important SNPs, right? Perhaps we start there and we say, “Okay, is there a way to cluster those markers?” Let’s say we’re looking at methylation, we’re looking at folate, we’re looking at something that is a system of the body. Can we gain more insight by looking at just the serum markers first, maybe B12, B6 folate, then maybe look at some methylation expressions and perhaps in the not too distant future, tying that together with some other markers in the microbiome, in the gut biome that also give us an additional story.

But that way, you’re keeping a common thread of what you’re actually trying to discover as a practitioner. Let’s say you’re trying to solve for a specific problem and then you draw data markers, but only those that are relevant to clustering into that story. As opposed to saying, “All right, let me throw through those 50 pages of genetic markets and see what I can pick out.” And I think that’s where if we take, again, the practitioner’s perspective, the user experience of the practitioner really, really suffers because they’re under the gun having to process more and more and more data in less and less and less time. And unfortunately, as you say, the data is exponential.

So, genetics was a great example of data running ahead of the capacity of the system to handle it. So, unfortunately on the consumer side, it morphed into very little more than some sort of an ancestry search, right? Now, in the cancer clinics, they do deep dives and they look at full sequencing and shotgun sequencing of certain SNPs. So, we are making progress in that sense, but to the man on the street or somebody who’s just having a healthy person interaction with the healthcare system, it’s going to be years and years before this comes into something that you can use. So, I think that usability of data and being able to translate it to the end consumer in a way that’ll actually relate to it is what’s missing.

James Maskell: So, before we get into some of what you’re doing there, just talk to me about what it takes to solve the PDF issue, because ultimately, everyone’s aware of it. If you’ve ever ordered a normal test or even the functional medicine test, it arrives on the PDF. They’re pretty standard, but they’re all different. So tell me about from your team over the last few years, when you first started to solve this, what was the sort of foundational layer of what you had to do to achieve an operating system that would make the PDF obsolete?

Ivan Vatchkov: Well look, at Collibra, we pride ourselves on trying to be as simple as possible. So I’m going to take an example of actually child’s play. And what we did is we said, “Okay, the collection of markers that is in this PDF is not unlike Lego bricks, right? You can take Lego bricks as individual things and you can stack them together in different ways to achieve sort of different structures. So the first step was to take all of the markers. Now, let’s just stick with blood work for the time being, because that’s very well understood by everybody, and functional medicine doctors love it, and traditional doctors love it. You can take all of the markers in a very elaborate blood work panel, and you can take them and individually create individual markers like Lego bricks that not only contain, okay, let’s say this is vitamin B12, but also create some sort of a smart metadata to that marker that makes it easier to process.

So for instance, what we do at Collibra is we take not only the marker, the definition and the reference range, but we also add another layer, which is we score that marker within a curve that goes sort of from optimal to normal and then to basically zero outside of the normal bounds. And this adds a layer of understanding, because now it’s machine-readable. It’s not only machine-readable in James Maskell B12 value was X, but what that means for James Maskell. James Maskell’ optimal value is X. So that scoring is really, really important because machines cannot understand data that is qualitative, but they can understand quantitative data very well. So step one is take all the markers, fragment them into something that is a bite-sized marker that is intelligent. It contains another bit of intelligence within itself. And then, that Lego brick is no different to another Lego brick, which is my resting heart rate, which is my screen time, which is my self-perceived measure of loneliness, which is my mindful minutes.

So step one is forget the structure, forget the source, break everything into markers and organize these markers into something intuitive rather than into the source that they come from. So to go back to the example we were discussing, maybe if I’m looking at a particular deficiency in a blood biomarker, I could draw in something from genetics and something from biome and something from lifestyle that helps me depixelate the picture of that particular issue further, but rather than having to scan for PDFs. And then we took that a step further and we said, “Okay, well, a collection of markers actually makes the problem worse for the practitioners because it goes from one PDF to 60 blood biomarkers. What do they do with this?”

But what we did with that is we have composite scoring that helps you take a look at one blood work PDF and basically quickly spot the scores in. Let’s say we use the functional medicine organization structure, okay, energy and metabolism, acid-based balance or something else. I can very quickly zoom into the one issue that is screaming at me from the PDF, rather than having to spend five or six minutes with a pencil looking through and say, “Okay, this is five, it should be six.”

So to sum it up, first, bite-size the data into bricks that are interoperable anywhere. Because for instance, cortisol informs just as much about physical health as it does about mental health very often. My stress marker can be often cortisol. So if I’m looking at a composite person, I could very well decide to use cortisol elsewhere, but now I have to use it in this fixed framework. So bite-sized markers and having an operating system of how to prioritize what’s most important so that what we surface to the practitioner is, “Okay, look, you know 100 things about these clients, ignore 97 of them because they’re normal, optimal, they’re not relevant, or they are lower priority. But right now, what you need to know is he’s got low vitamin D, his deep sleep is 35 minutes a night, which is very, very low, and he’s severely dehydrated as evidenced by his water consumption and also by some other markers in his blood work.” I think that would be-

James Maskell: Hey, how did you know that my water consumption was so low?

Ivan Vatchkov: I was speculating. And the sleep was obviously an example that I wouldn’t know anything about anyways.

James Maskell: Well, it’s interesting. So I was the patient, I am a patient of a functional medicine doctor. So yes, it’s about what’s interesting and relevant and all that stuff, but it’s also about why did I come in? Because if I came in for this condition, yes, these other things may be wrong and we can get to them, but ultimately I want to get out of pain or I want to get out of symptoms quickly, get at the issue. So a lot of times, the practitioner is prioritizing based on, “How do I get six months to really work with this person? Will I need to like prioritize at least one thing for them quickly?” And so, that can be useful as well.

Ivan Vatchkov: No, absolutely. And I think the practitioners very often and correctly take the tack of, “Hey, give me a problem to solve. Don’t tell me your life story why you’re sitting here.” But again, if we just take again the traditional medicine or functional medicine example, the way that they will gather this information is in one of two ways. Either they’re going to ask you to do some sort of a structured, validated survey of some sort, a PHQ-9 or something sleep-related, that again, surprise surprise, is a PDF that he has to review. So this isn’t summarized or made intelligent or living data in any way, or they’re going to spend time in the little time that they have, asking you very, very quick questions to try to triage exactly how to bucket you.

But in the heat of the moment, the doctor is not being paid for their judgment. They’re being paid for their time because they have to spend that time to invest to do data gathering and triage. The doctors that we speak to find this incredibly frustrating because they don’t want to be data clerks, they don’t want to write a lot of notes. And now that open notes project is in place, they have to be very careful about what they write. So instead of using a very, very smart and sharp tool, which is the practitioner’s mind to solve a specific problem, there’s more and more data collection and admin that’s ballooning.

So how do we allow for machine learning or processing or can we say AI, to summarize this into something where I walk in and you say, “Okay, doctor, Ivan’s complaining of knee pain, ACL cruciate ligament surgery three years ago. He’s magnesium deficient and he runs 18 hours a week.” So having that contextual information would be incredibly useful to a doctor at a glance, in some sort of a consult like we have at Collibra Pro, rather than, “All right, doctor, here is my ACL report. Here is my diary of my running stats, and here is my form that I filled then about knee pain. Good luck deciphering this.” It’s just a very poor user experience. And I think both the practitioner and the consumer or the patient recognize this.

James Maskell: Absolutely. Well, yeah. Look, so I want to go a bit deeper into that because not only is the data collection annoying, but then it’s like, “Well, what do we do with that information?” And when you’re a functional medicine practitioner, you’re caught between two things.

On one hand you have to shore up the bottom of the matrix. You have to get people doing the baseline healthy behaviors. And someone’s healthy behavior is already at a 70%. And so, what they need to do to improve is different from the person that’s at 10%. And so there are tools that are being created to try and understand how bad is your diet actually now so that we can make it a little bit better and contextualize it into how far along you are from McDonald’s every day to eating everything at home and having perfect anti-inflammatory diet.

So there’s all those things, but then on top of that behavior change around lifestyle, there’s also the protocol, which is some individualized thing on top. So I guess I’d love to hear your thoughts on how you came up with, now that you’re getting all that data, how do you prioritize to tell people what to do and how do you understand what are the foundational things that need to be prioritized?

Ivan Vatchkov: Thank you. And there’s a lot to unpack there, but you’re absolutely right. We all need different things in different proportions at different times, even though our bodies and mind work in a similar way. And look, my background is in financial services where we handle uncertainty on a daily basis and we handle high volumes of data. So we took a fairly irreverent approach here, which is to say the only way that I can structure and sort data into something useful is to actually score it, to give it a hierarchy, a priority. And that doesn’t really chime with medicine, traditional medicine or even functional medicine very well, because it’s very difficult to arrive at certainty and objective approval certainty of why this is more important than that. And you could probably find a doctor that’s going to argue it either way. So you have to be very, very careful there.

But our approach has been to actually score every individual marker, create a neural network of all those markers with different levels of importance and priority. So we know that perhaps sleep is one of the superpowers that we have. So fixing sleep and exercise is perhaps more important than something else. So the way we approached it was to say, “Okay, from all of the markers, let’s group them into groups, give those groups relative weights so that we can say to the practitioner, ‘Look, this is not a diagnostic tool, but from what we are seeing, this is the full picture of the client. These are the dashboards. What we think is worth investigating is Ivan’s protein intake, his exercise regimen and his micronutrient deficiency. And for James Maskell, it might be his deep sleep, his hydration, and something else.'” So what we try to do is introduce hierarchical stuff, hierarchical ordering of markers so that we always know what the most important thing is.

Now, not every practitioner is going to agree with this approach, and there’s much work to be done to validate this. But what it does do is save you 90, 95% of the time you would spend investigating and scanning PDFs. So that sort of step one is a rank order of the potential markers or issues or areas that I would have to explore as a practitioner on my dashboard, ideally before the client even shows up. So I think that would be a very good advancement. And it’s not as simple as just taking a dashboard summary of a bunch of wearable markers and a bunch of, let’s say, blood biomarkers, because dashboarding PDF certainly helps in terms of visualization, but it doesn’t help in rank order of priorities.

I need to know that HB1C is the most important blood biomarker to look at, and I need to know that out of the 25 things that my wearable is beaming, actually HRV and deep sleep are the things to focus on for James Maskell. So that step one. Step two is allowing the client to augment that data by supplying subjective data. For instance, maybe my Oura ring is telling me every day that my sleep is 95, 2 and a half hours of deep sleep, one and a half of RAM, no wake-ups, beautiful eight and a half hours in bed. But I wake up every morning and I feel absolutely dead tired.

If I’m able to report my restfulness subjectively to my practitioner, he might conclude that while my sleep hygiene is fantastic, I’m lacking something somewhere else that needs addressing. Again, that could take them 45 minutes to discover in a conversation scrolling through my Oura Ring app, which of course he doesn’t have on his console, et cetera, et cetera. So that step two is to allow this objective to augment the objective, recognizing that on the one hand it can supplement the data, on the other hand, it can really confuse things. So I think that’s the bit that’s missing that we are trying to push the boundaries on.

James Maskell: One of the things that strikes me when you’re speaking here and what struck me ever since is that what you’re saying sounds complicated and complex, but actually what you deliver is simple and the work that practitioners are doing seems simple, but actually it’s extraordinary, complicated and complex in the current state, the current state of practicing because of all that data. And so the output, and when I understand it in that way, I have the feeling that after tools like Kalibra enter the marketplace, medicine isn’t really ever the same again because you’re either using something that really organizes everything into a skillful prioritization or you’re not. And if you’re not, the user experience with the patient is unlikely to be good and the recommendations are going to be all over the place.

Ivan Vatchkov: Exactly. And look, I dream of the day where we actually recognize, especially in the US, the patient are not as the third party in the payer patient structure, but actually as somebody who is the CEO of their own health over there to support them. But you make a very good point, which is the practitioner is absolutely critical. The mind and the eyes of the doctor are an incredibly complex system that I think as much as we love AI machine learning, it’s going to be near impossible to replace. When you walk into that office and he sees your gate, he sees the color of your face, he sees your eyes and everything else, there is no way we will ever be able to replace this. It’s an incredible sensory and mind combination that is difficult to replace, but we need to use it for its purpose, which is to really find the one thing.

And today doctors do this unconsciously. They scan stuff, they read it and store it and a pattern starts to emerge. But as we’re severely under pressure with respect to capacity in healthcare practitioners, we need to find a way to take some of that cognitive load of them. So I don’t think AI ever replaces practitioners, but I think it does replace practitioners who don’t use AI and augmented intelligence to just arrive at conclusions faster or take all of the data into consideration so that their cognitive biases or their tiredness or the fact that they hadn’t read up on the latest PubMed notes is somehow taken into consideration.

James Maskell: Yeah, well look, I think that’s a great dive. I mean, I’d say for every practitioner who’s listening, having a way to sort in real time, all information that’s coming in to your office, all the information that your patient wants to give you, things that you can connect in there like wearables and other information into something that is super critical. And there are a couple of other things that are doing similar types of things that I have heard of, but the thing that really got me and the reason why you are here on the podcast, and I’ve been waiting actually since we met to have this podcast, and I’m really excited about it, is that you’ve got to step further with it, which is really to identify how do you create the continuous engagement with someone or something from the clinic so that the user experience and the chance of someone actually doing the behavior change is at its highest.

And I would just give my examples, we’ve been over the years, huge proponents of coaches, right? Because health coaches, time efficient, they’re really working on, like if the doctor’s coming up with a plan, there’s another person who’s implementing the plan. And both of those things are necessary. And we’ve gone so far, I mean, I’ve gone so far to work really hard on create a continuous container for people to be supported with the health coach, but also with this virtual group that goes on for 24 weeks, and so the patient has multiple different touchpoints. But I want you to share with the audience what you’ve created because ultimately the combination of what you just shared, which is bringing all the information together and what you’re about to share, I think is the thing that gets me super excited and I think will change medicine. And I hope to support I in changing medicine, but that is Kali. So tell us about that.

Ivan Vatchkov: Well, thank you. That’s a very kind of you to say. So there’s two problems in how we approach healthcare, not sick care, but the healthy person’s journey. One is the data, the management, and the support, which is something we’ve discussed at length. And the other one is actually probably the more important one, which is generally low engagement and low compliance. And this ranges from some really extreme examples where people with critical illnesses forget to take their pills or neglect to follow a protocol to something on the other edge where they’re worried well don’t follow ambitious protocols that they’ve set for themselves. And accountability, partnership and support is absolutely critical, I agree, which is why a community and a practitioner is important, but it’s also important to figure out a way to drive engagement. And you mentioned it, I think really well.

We need more touchpoints. Continuous care is continuous for that reason that you need touchpoints to remind, to engage, to nudge, to cajole. And I met a practitioner yesterday, she was saying, I want my colleague to scare me into action. Hey, your HRV is too low, start focusing on this immediately. So we took all this complexity of scoring markers and organizing them into groups and hierarchical neural networks, et cetera, et cetera, stuff that’s really not going to resonate with the consumer and hit it behind a very simple conversation interface called Kali that every day touches base with you with a very simple conversation structure, which is it gives you an insight about you based on your data. It could be, hey, your rest scores are low, so let’s talk about sleep hygiene today. So it gives you a little insight. It asks you a question about a particular marker or a particular pillar of health that we’re interested in.

Could be your connection with your community or your movement patterns or your sleep hygiene or something else. And then at the end of that assigns an action. So the typical interaction would be, hey James Maskell, they see that you are not sleeping well recently, of course, I’m speculating. Let’s try this tonight. Three hours before bed, no food, two hours before bed, no work, one hour before bed, no screens. And then let’s see, one, did you actually do it to the next day, we’re going to ask you, hey, James Maskell, what did you think of the three, two, one protocol? Secondly, we’re going to see if you did do it for two or three days, did it actually make a difference to your deep sleep or to the number of wake up or to your respiratory and resting heart rate at night? And if so, we’ve just created a micro block of a behavioral loop on which a habit can be built.

Now, I shouldn’t sugarcoat this, this is incredibly difficult. We know the human nature is very difficult to change, and even with the highest of motivations, people have an engagement curve that drops precipitously after a few days. Just like anybody who tried to do use a food logger would know despite your best motivations to lose weight on day three or day five, you give up on this. So how do you drive those engagement touchpoints and how do you keep the client tethered? To us, it has to be an intimately human approach, and we are already all conditioned to use the medium of chat. We check our WhatsApp very often. We’re very used to interacting with chat. So if we could build a virtual coach that is personalized, engaging, but balance is being the mother on your shoulder with the angel on your shoulder without knowing you too much or being too soft, I think we’ve got something.

So we try to take all of those markers and say, hey, don’t worry about checking your Oura Ring dashboards every morning, don’t worry about looking at your garment running data. Don’t worry about all of these things. We will try to parse the data for you, surface a marker or an insight or an action that can translate your health journey into a simple step by step thing. Because most of our consumers say, look, it’s all great that you guys have the science and you’ve parsed the data. And if I could understood your AI, I would be perhaps excited about it, but I don’t care about any of this. I need to be told what to do in real time. So for me, it’s not about actionable insight, it’s about literally having no choice but to be told what to do every day. So consumers don’t want more choice.

They want confidence in the choices they’re making. And if you can augment those choices with the data and actually say, look, because Yvonne’s deep sleep is only averaging 35 minutes, we need to do three, two, one protocol. We hope that this will resonate a little bit. Now, are we there as a company? Absolutely not. I think it’s going to be a very, very long journey of iteration improvement and refinement. But at least our bet is that taking complex data and hiding it behind a human conversation, which is how we mostly interact with health, whether we ask for recommendation or for a protocol or something else is helpful. And now that most doctors and Peter OT is about to publish a book, talk about their own protocols, it’s very difficult for somebody to take a protocol from a book and translate it into daily action.

But if we can take all of the actions from that protocol and tag them to the customer’s particular condition, so somebody’s sleeping really well, don’t worry about sleep hygiene, focus on their movement. If they’re moving and sleeping well, focus on their protein intake. If all of those are there, consider mindfulness and consider their purpose and potentially the strength of their connections, which is a key input into longevity. So being able to be reductionist in how you approach the individual customer journey is helpful. And nothing is more reductionist than forcing that into a three or four block conversation for 30 or 40 seconds every day. So our bet is that initially this is a good way to start to improve the cadence of engagement and hopefully build habit loops on the back of that.

And we’re working with behavioral scientists on how to improve those hooks and how to strike the right balance between invasiveness and intriguing the client and stimulating them to do something. And then we can obviously at the end, drafting the social aspects of, hey, James Maskell, Yvonne is doing 50 burpees, are you in? That could be another aspect of it, but candidly, we’re not there yet at all. But having conversation blocks that are personalized hopefully takes the conversation a little bit further With respect to health.

James Maskell: So where are you now? So give us an idea. We have doctors from around the world. We have a lot of functional medicine doctors in America. We have a lot of other types of practitioners that are interested in functional medicine. We have people who work in hospitals and other that are practicing that dream of practicing differently or are happy with their practicing and trying to evolve it internally. So there’s a wide range. We have a lot of health coaches and other practitioners that listen in. So where are you guys right now and, well, who’s a practitioner that’s a good fit for Kalibra?

Ivan Vatchkov: So where we are as a company is we launched February 1st. We’re based in Singapore, but we work globally. We are looking at three types of target clients. One is big wellness clinics within big hospitals, and we’ve onboarded one of those already, and some of the marquee names have reached out I’m proud to say. And after demos, I think we’ve got a good pipeline of those. So there’s one aspect of it, which is we already have the customer footprint, but we’re looking to introduce longevity as a service or health plan as a service and improve the user experience. So that is one group, of course, that’s the most lucrative and big, but they will be the slowest to change because it’s going to be inertia from a big organization. Then there’s the functional medicine slash integrated practitioners who are fantastic.

It’s a clinic of between one and four doctors who are really dialed in on the advanced lab testing, on the customer journey and individual touch. They charge a premium price, but the outcomes they get are fantastic. We’re at the moment balancing mostly towards those practitioners. And the last group is what I would define as health coaches more broadly, which is elite personal trainers, elite nutritionists, and other lifestyle coaches that might be the type of person that actually comes into your home and looks at what you have in the kitchen and checks for mold in your bathroom and checks for EMF radiation, people that really push the boundaries of changing our environment. So those be the three target groups. We’ve already been approached by a fourth group, which is Wellness Hotels & Spas who are looking to create that connectivity and last engagement beyond the client visiting them, but also to create a continuity of the journey.

Okay, you come here every three months, how can we stay tethered to you for the three months that you are away? So those are the three target groups. We’ve been, I suppose, pleasantly surprised with the incoming. And at the moment, we are parsing the incoming to select really the people that we feel resonate the most with our ethos, which is health is more than just physical, it’s mental, social and emotional. How can we get clients to recognize this and address all of those things in some sort of a priority?

So we’re onboarding practitioners right now globally, and it’s our job to find the right fit because we want to do an initial footprint and then consolidate, take feedback and improve, rather than blitz scale to a level where we won’t be able to individually onboard doctors, which is what we’re doing now, spend time with them, understand the user journey and take real time feedback, which goes directly into code. So I would say we’re towards the tail end of completing our footprint with our, what I would call lighthouse clients who will spend the next three to four or five months with working alongside their practice, but also their user journey and having that ongoing engagement to really see whether some of the tools we will be able resonate with the journeys that they have or whether they need to be changed. We’re a very young company. We’ve got a lot to learn.

James Maskell: Well, awesome. Well, look, I’m sharing here that if you’re listening to this, if you go to goevomed.com/kalibra, you can be one of the first groups to get in touch with Kalibra and start to work with it. I’m super excited this for a few reasons. One is, first and foremost, the name of your virtual assistant is the name of my daughter. And I know that-

Ivan Vatchkov: Very good to hear.

James Maskell: Just the idea that someone called Kali, with that name, with that energy, which is what I gave my daughter, just resonates with me so deeply. I think the second thing is that you told me, basically you were going to do this in 2015 and it took you all this time to get here, but ultimately you didn’t really know what it was, but just to see you follow through. When we met eight years ago, now to see you follow through and actually create this thing is incredible. And thirdly, having been a patient of functional medicine doctors and also worked with thousands of clinics, I just recognize so much of the overwhelm that practitioners and doctors feel is because the data is a mess. And more data from practitioners … When you go into functional medicine, you get all this new data, which is exciting, and you have to change your business model to have a four-hour initial appointment. We will never get where we need to go with the way that it’s being done right now.

Ivan Vatchkov: There aren’t enough-

James Maskell: I remember at the beginning of the functional forum, I was like, “Look, the thing that really solves all of this has not been made yet.” And I don’t know if this is the thing, but it’s got a lot of hallmarks that might be the thing because of the behavior change element and the data aggregation and prioritization. And really what is functional medicine? Functional medicine is an operating system to organize and prioritize data. That’s what it is. That’s what they came up with. Those guys sat around and created a way with the matrix and the system to prioritize and organize data in such a way that you could make sense of it, and it was amazing, and yet it was made in the eighties. And so ultimately the opportunity here is to take that to the next level. And that’s why I’m super excited because I feel like this has the potential to fulfill the potential of our functional medicine as an operating system that could really transform the way that people engage with their own health, and I’m 100% behind it.

I’m really grateful for what you’ve done, what your team has done, and I’m super excited to be a witness to this journey over the next few years to see how good it gets and all the different places that it can be deployed. And I personally expect to see it being just deployed in functional medicine offices, in serious clinics, but also in health systems because I’m dealing with this every day. They’re not in the business of health creation, but they want to be. They at least know that it’s possible, and I think this could be a stepping stone towards that too.

Ivan Vatchkov: No, absolutely. Look, baby steps will keep pushing the agenda. I think what’s really interesting to share is that Singapore is moving in that direction already. They’re saying, “Every GP should be responsible for the overall health of their clients and not be paid for the procedures they perform, but rather keeping their client healthy.” That is a radical departure of how we’ve been set up over the past 50 or 100 years. And the toolkit is there already. It’s functional medicine, it’s understanding the client intimate needs, personalized, individualized approaches. But how do you scale this? Well, it has to be through software. And hopefully that’s where we can make a meaningful contribution.

James Maskell: Beautiful. All right, well check out goevomed.com/kalibra K-A-L-I-B-R-A. We’ll have the links in the show notes. Ivan, such a pleasure. I’m excited to see you go on this journey and to be part of it. And thanks so much everyone for tuning in. Look, this is the Evolution of Medicine Podcast. We’re talking about evolutionary concepts within medicine. We’re talking about practice management, we’re talking about technology. This is all of those things in one. It’s a perfect fit for the podcast. I’m glad you’re here. Thanks so much for listening and we’ll see you next time.

Thanks for listening to the evolution of medicine podcast. Please share this with colleagues who need to hear it. Thanks so much to our sponsors, the Lifestyle Matrix Resource Center. This podcast is really possible because of them. Please visit goevomed.com/lmrc to find out more about their clinical tools like the group visit toolkit. That’s goevomed.com/lmrc. Thanks so much for listening and we’ll see you next time.

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