The Physical of the Future with Jeff Kaditz, Founder and CEO of Q Bio: Audio automatically transcribed by Sonix

The Physical of the Future with Jeff Kaditz, Founder and CEO of Q Bio: this mp3 audio file was automatically transcribed by Sonix with the best speech-to-text algorithms. This transcript may contain errors.

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Saul Marquez:
Welcome back to the Outcomes Rocket everyone, Saul Marquez here. Today, I have the privilege of hosting the outstanding Jeff Kaditz. He’s the founder and CEO of Q Bio. Jeff is a serial entrepreneur, angel investor and adviser who has helped drive a wide range of technologies and businesses. His field of exploration has included rockets, high energy particle physics, anti ICBM technologies, consumer electronics, mobile gaming, finance, enterprise software, network security and biotechnology. So I’m excited to dive into the work that he and his team are up to at Q Bio. They’re working on the physical of the future. So, Jeff, thanks for joining us today.

Jeff Kaditz:
Thanks for having me. Great to meet you.

Saul Marquez:
Likewise. So what is it that inspires your work in health care, Jeff?

Jeff Kaditz:
I think a couple of things. I think one is it’s a little bit selfish, but I just have seen both. I myself, but also members of my family had less than great experience with health care. And I don’t think that we’re anywhere unique. And it just seems like a place where we have a lot of room to improve. And I think that especially if you have the luxury of not worrying about tomorrow, which I think is a luxury for some of us, then I think the best thing you can do is to use that time to think about how you can make tomorrow better for other people. So that’s, I think, a big part of it for me, because if you don’t, there’s a huge amount of people who have to worry about tomorrow and so they can’t or they have to worry about today so they can’t worry about tomorrow. And so I think that that it’s those of us who have that privilege. I think it’s our job to to try and make tomorrow better. I also am just particularly fascinated by the human body. I think we understand the rest of our universe better than we understand what’s going on inside of ourselves. And to me, as somebody who’s done scientific research on exploring different parts of the universe. I keep coming back to the fact that it’s amazing how little we actually understand what’s going on in inner space versus outer space. So I think that is another thing that inspires me.

Saul Marquez:
I love it. Yeah, that’s really great. And so appreciate you and the approach you’re taking with Q Bio and how we can take care of ourselves, the luxury of not having to think about tomorrow, but thinking about tomorrow. How are you doing it. What are you offering that’s unique.

Jeff Kaditz:
Well I think, first of all, I think what we’re doing right now has been really a in some research to help us understand the requirements for what we ultimately want to do. But what’s unique about us right now is, I can comfortably say that we can gather more quantitative information about a human body in a shorter period of time for cheaper than anyone else. As a result of that, we are also able to very efficiently understand what’s changing in a person. And I don’t mean in a single dimension, but I mean in a very comprehensive way. And then lastly, we’re able to actually prioritize the importance of what’s changing in a person contextually based on that person’s medical history or genetic risks. So I think it’s those three things currently that we’re doing that make us very unique. But I think that is the kind of tip of the iceberg for where we really were really trying to do.

Saul Marquez:
Fascinating. So talk to us about the approach. You’ve got this Q scan, the Q membership, why you and tell us a little bit more about that.

Jeff Kaditz:
It’s pretty dorky. It’s simply quantitative. That’s really what I think. It’s really I mean, everybody talks about medicine as an art, but I strongly believe we can make it a science. And part of that the first step of that is actually quantifying our biology and does a lot of tools. And we’re entering this age of kind of biological digitization.

Jeff Kaditz:
And obviously, I think the kind of asymptotically you can kind of say are will inevitably we’re going to have digital twins, Right. Or models of our physiology that are refit during a physical and so that we can make individualized forecasts about a person the same way we make forecasts about the weather today. But I think that that’s where the quantitative part comes from, though, really is the first step was how efficiently can we quantify our biological state?

Jeff Kaditz:
Do we have all the tools we need so that in a short period of time I can take a snapshot of our biological state and a reproducible way so that I can then start to measure what’s changing. And this isn’t really a new idea. Honestly, this is typically called the scientific method. If I can take any scientific discipline that’s arisen in the last really thousand years or so and the really when things become pseudosciences or turn from pseudosciences into science is really when they become information sciences. Like you talk about the image of the telescope. What that did was allowed someone like take a broader take hundreds and probably thousands of pages of recordings of the positions of the planets. 400 years later, Kepler came along and fit those measurements to elliptical orbit so that he could predict the position of the planets. The thermometer revolutionized our ability to predict the weather and obviously combining humidity and pressure and wind direction allowed us to make forecasts for the weather.

Jeff Kaditz:
So really, the trend in all these places, when we develop the tools to measure a system in a commodity way, like in a cheap way, we can then measure how that system is evolving and changing, and then we can apply mathematical principles to those systems to make forecasts about the next measurement. And when those measurements fit, like when the prediction fits the next measurement, we say we’re starting to understand the rules that govern the system. And if you think about what understanding diseases in a person, it’s really no different. It’s can we understand changes so that we could forecast the next change rather than wait for it to happen so that we could potentially intervene early? And so really, I think what we’re doing is taking a method that’s really been used to help us understand every single part of our world and applying it to our bodies.

Saul Marquez:
That’s fascinating and truly different. I mean, I talk to a lot of people, Jeff, and the approaches are iterative. And I think this approach is definitely unique. Although you say it’s nothing innovative, the approach is great. I personally am a freak about numbers and measurements, so I love this. I actually do a DEXA scan twice a year. I like to know my numbers. Right. And so I’m fascinated by this. So walk us through it. You take an MRI and you do different things. Talk to us about the process.

Jeff Kaditz:
Right now, today at our at our kind of R&D facility in Redwood City, you can sign up for this service. And in about 60 minutes, we take blood, saliva, urine, comprehensive vitals and scan your entire body, noninvasive radiation. We also aggregate your medical history. So as the first in your first visit, we’ll also do a genetic profiling. After that, it’s we don’t use profiling, but after that, it’s every incremental visit is exact same for the most part. And we’re really just kind of looking at what’s changing and looking for trends or correlations between changes at a structural level or a chemical level, and then interpreting those changes in the context of your genetics or medical history or behavior. And so that’s really essentially what we’re doing as a first step here. But it’s the goal is to make this actually a standard much faster, much cheaper, so that you could deploy this to entire populations. And we actually are very close to being able to do that. I think actually within two years, this is the kind of thing that you could deploy to an entire population.

Saul Marquez:
That’s exciting. And so you go in for your first measurement of these various things. And I mean, can you tell anything from that first one compared to your genetics or is it is the power and in the next visit where you could see compared to baseline?

Jeff Kaditz:
Yeah, well, so I think a lot of people might look at this as kind of fancy screening. I think our approach is actually very different as you bring up. A good point is can you tell anything from the first? I think you can. But part of our philosophy really is that we don’t believe in absolutes, Right. and a single measurement at a single point in time compared to something like population average, which fundamentally I could go into much more detail, I think is a flawed concept. The idea of a population average or population reference, especially in the era of we all have unique genetics. It gets more interesting as soon as you start seeing trends. But from an information, theoretical perspective, it’s also more interesting because you’re boosting the signal to noise. If you’re a person who’s worked in EE or done a lot of any kind of signal processing or information theory, you understand that individual noises, even if there’s a lot of accuracy and precision, you can be measuring just noisy systems and especially things like your chemistry, like those things very rapidly. And so by taking multiple measurements, even if they’re spaced out over time, you’re effectively boosting the signal to noise ratio and looking at trends in those minor like the jostling and the vibrations, the oscillations you can kind of ignore. And so I think that’s that that’s really powerful. And again, it’s not a new idea. It’s saying, look, the signals that are changes in our body are roughly sinusoidal in some way.

Jeff Kaditz:
Lots of them. We’re pulsing like we grow and expand every day. Like that’s that’s fine. But what that really means is the idea of measuring us at a single point in time and comparing us to some reference rather than understanding like general trends, I think is the wrong way to go about characterizing. Here’s a good the analogy I’ve used in the past is imagine if I played you a single note on a piano and then said, tell me which song that’s from. That’s pretty hard, Right.. Well, what if I played you a series of notes, then you can probably start to say, oh yeah, no, I recognize that tune and that’s because a song is actually made up of many different notes played in some time series order. I think ultimately we’re going to discover the disease. Disease is really like a song. There’s a progression. And if you try and capture a single measurement, it’s important time. Many songs can have the same note Right., but it takes a little bit more information from you to uniquely fingerprint a song. And if Shazam try to play one note in Shazam and say, tell me what song that was. I promise you it won’t work now.

Saul Marquez:
Yeah, it makes sense. And I think this approach is key and being proactive about our health is important. Everybody has to go. If you’re doing it right, you’re going to go to your your primary care once a year. If you’re healthy, normal, you’re going to go you get your checkup and get everything, make sure everything’s in order.But now you’re offering something that’s much more in-depth. What would you say is the key difference and why?

Jeff Kaditz:
Well, I actually don’t think we should go to a doctor once a year. I think on average we should go less.

Saul Marquez:
OK.

Jeff Kaditz:
And I think that I mean, this gets to right at the heart of I think the key problem that we’re ultimately trying to solve that we haven’t totally really unveiled the platform to solve this problem. But the fundamental problem that I see in health care is that we are simply as a population growing faster than we create doctors.

Saul Marquez:
Yeah.

Jeff Kaditz:
That’s just true. Not only that most doctors are opting to if you’re a general practitioner, you’re opting for smaller practices because you want to spend time with fewer people. And that really is giving rise to concierge medicine. Or you go into a highly specialized area because you get paid better. But the kind of massive 2000 person panel, like no doctor really wants that. But the problem is, is that when you talk to doctors about preventive medicine, it’s like, OK, well, people need to spend I need to spend more time with my patients. That’s fundamentally flawed. Like, really, that approach simply doesn’t work. If you just take the original thing that I said, which is the population is growing faster in the number of doctors, that literally means we need solutions in health care that allow us to better utilize the most scarce resources we have, which is a doctor’s time. So to me, the ultimate solution in primary care would be a system that could automatically tell doctors who they should spend time with and who doesn’t need their attention this year, because that could effectively scale a doctor’s time from caring for a thousand people to ten thousand people, because maybe only one or two out of ten people need a doctor’s attention in a given year.

Jeff Kaditz:
So it’s impossible for doctors to just uniformly say, I’m going to spend three hours a year. And I see there’s companies that are being started right now that say our doctors will see you as many times you want this year and spent at least three hours. So that’s great if you have the money. But all he’s doing is accelerating health care inequity because the way health care is delivered right now is it’s either first come, first served or who can pay the most or who has connections if we want. And COVID has really highlighted this. We have completely failed to deliver care to those who need it first. And so the real missing part of health care is how can we automating human labor extremely hard? I don’t care what anybody says in AI. How do we automate how labor is used? I think that’s the key question.

Saul Marquez:
Yeah, yeah, yeah, yeah. That’s an interesting approach, Jeff, for sure. You know, it’s unique. And when you take this approach at the population health level, it certainly becomes an efficient way of how to scale a doctor’s time.

Jeff Kaditz:
We’ll put it this way is like COVID. I mean, we’ve been building we’ve been thinking about this for I’ve been thinking about this for better part of a decade or more even. But COVID has honestly been one of the unfortunately a great example for to prove all the points that early on we were trying to make, because we want to like testing, think about COVIS testing. We want to effectively test massive parts of the population. What do people do, drive through testing? Is there a doctor there know? Why would you have a doctor? Why would you have highly skilled labor doing gathering information that like an unskilled technician together or like a somebody with not ten years of medical school. So in fifteen minutes or twenty minutes, I can get my COVID antigen test right now as a drive-through. What if I said, why don’t we do the things in thing for your whole body? What if in twenty minutes I could measure everything about your entire body, you go home and you get a call from the doctor? Only if there’s a problem. If you don’t if you don’t hear from the doctor, you’re fine. Why not? I mean, we know that I mean, literally, we know that this is the most effective way to roll out like health care or especially collecting information about people. Why wouldn’t we conduct a physical exam the same way?

Saul Marquez:
Yeah, yeah, yeah. No, it makes sense. And maybe it’s just the mindset that it’s ingrained in us that we got to go to the doctor once a year. Like maybe we don’t.

Jeff Kaditz:
Well, I mean, think about a doctor’s time is not cheap. So if you spend time, think about how much information a doctor can gather. They can ask you some questions. They can look at you and those can those are all valuable things, but. I have a hard time believing, given where we are in modern sensor technology, that it will be possible or even it is right now for a possible for a doctor to gather more information than we can in 20 minutes. And so why would you if doctors real unique skill is integrating that data and then making care decisions. Why wouldn’t we separate the gathering of information from making care decisions rather than conflating them now?

Saul Marquez:
Now, this is great. And folks, I love this discussion because it’s different and there are a lot of assumptions that we make in health care. And I love Jeff’s approach to this problem of scaling doctors that we definitely have a big issue in this country. And I welcome this fresh approach. So as you guys have built the tech and the company, what would you say has been one of the biggest setbacks you’ve run into? And how has that made you guys even better?

Jeff Kaditz:
Biggest setbacks? That’s a good question. You know, in some ways I was anticipating more setbacks. But one of the biggest surprises, let me say, with the biggest surprises me, were setbacks. So one of the biggest surprises to me was I expected a lot more pushback from doctors, and I learned a lot talking to a lot of doctors, but they’re really scared of and how they really view what we’re doing. And I think and it made me much more optimistic because I realized how doctors know, how broken doctors know they can’t work harder. They’re like, we don’t know how to work smarter. And it’s not not to say that they’re helpless in any way, but they know that what they’re doing simply doesn’t scale. And they want new ideas and they want to try new things. But they also work within, in some ways handicapped because they work in a system where trying new things, the only thing you can do is harm them. They are heavily incentivized to take zero risks, even if the risk is extremely small. But they want to do better. I mean, think about it. If you if you become a doctor, I don’t want to overgeneralize, but in general, it’s a noble thing to do. It’s like I want to take care of people. I want to help people who feel helpless because and I think that’s a noble thing. What I have found is there is a there are some very large companies in health care and some very anticompetitive behaviors that have made it extremely difficult for us to do what we want and not to call anybody out but imagine if the equivalent would be imagine if Apple said that any application that you developed for the iPhone, we co-own. The biggest companies in health care. That’s their approach to kind of develop ecosystems or people who want to add value on top of their products or their platforms. They think in a very zero sum way, and that is rather than what is going to move care forward and how can we help more people? And I think the reason is because these guys are typically so entrenched that they’re more scared of preventing other people from innovating than they are focused on innovating themselves.

Jeff Kaditz:
But it’s amazing. Like, I actually don’t know some of the co licensing that occurs with these big companies and the anti-competitive behavior that happens. I’m shocked that it’s not investigated because it’s I think it does more harm than almost any other part of the health care system that I’m aware of. But, yeah, I mean, it’s crazy. It’s like this. You’re completely incentivized to work with some of these technologies, their technology platforms, because, again, like I said, imagine if part of the Apple’s developer agreement was we own whatever code you write for our platform. That’s literally what these people do.

Saul Marquez:
Yeah. Yeah. And so you’ve been surprised that physicians haven’t pushed back as much, that they’re welcoming this approach?

Jeff Kaditz:
Yeah, well, I think that the biggest the number one fear that I hear is around number two, most common things are physicians feeling like they’re already overworked. So it’s like if you give me all this information, it’s just going, I don’t know what to do with it. It’s going to be information overload. I think the other one is false positives. And I think we’ve done a pretty good job addressing that specifically with information overload. I think we’ve shown people that with modern software, you can summarize an enormous amount of information quite effectively. And I’ll give you my favorite example in the world, Google. Like when I search Google, it searches trillions of documents in, summarizes the most important ones for me. So if Google can summarize in a single page the most important, most relevant things for my question, I think we should be able to solve this problem for doctors. And so I think that doctors have seen they now start to believe that the software doesn’t have to be AI or scary. It can just be a tool that helps them do their job. Again, like the same way I think of Google helps me find what I’m looking for.

Saul Marquez:
Yeah, I’m totally digging it. I think it’s the opportunity is big.

Jeff Kaditz:
The second thing as far as the false positives is when you are. Looking at a lot of false positives come because when we look at a human body and all human bodies are somewhat unique, there might be some platonic form that represents a male body and a female body, but they’re all a little bit different. They really get along. It’s a very long tail. As we develop these tools to look inside of us, everybody is going to look a little bit different. And that means that having prior information of what your body looks like at a time before is so critical because if it hasn’t changed or if it’s stable, it’s probably not a problem. Right. But a lot of false positives occur simply because the first time a doctor looks inside of you, they see something that might be out of some normal or reference range, but you might have been born that way.

Saul Marquez:
Right.

Jeff Kaditz:
And so if you can, it really comes down to, again, like there’s a lot more signal and what’s changed than what hasn’t or seen any single measurement. And so I think that is the mindset. And actually, there’s an interesting thought experiment that I’ve done with some doctors who have said that some doctors, not all say that they don’t want to create anxiety in a patient who if they while they’re waiting for the results. But I would argue let me give you a spin on the service that we’re kind of that we’ve been piloting with individuals and doctors. What if you could do a Q exam, but we created a time capsule for you and didn’t allow anybody to see your results. We just stored them. So once a year you came in, we gather all this information we just stored up and no one could look at it. I would argue that just doing this will be extremely valuable for any individual. And what if the criteria for cracking this thing open was that you had a symptom, some symptomatic issue? Because that’s when this value of this data becomes the most valuable is when a doctor is effectively trying to troubleshoot a problem. And so one of the things I said doctors, is if you’re worried about or even individuals are saying, I’m worried about what I’ll find and say, well, I would argue that just collecting this data and not looking at it is a really good insurance policy because there’s going to be a day because it’s like death and taxes.

Jeff Kaditz:
I’ll tell you what’s inevitable. All of us will get sick and injured, all of us. And if I have a versioned history of the evolution of my body, its structure, its chemistry, and I can show that to a doctor who’s trying to figure out why all of a sudden my right arm is numb, I guarantee you they’re going to figure out what’s wrong a lot faster if they have this information to start with versus the first time you have a major health issue. It’s like they start gathering data, right, because and then they do this test in this test. And then before you know it, it’s nine months. And you’ve seen four specialists before they kind of get to the problem. And you just have to hope that it’s not the kind of problem that in nine months can get ten times worse.

Saul Marquez:
Yeah, that’s interesting. And I totally agree. Right. having that that baseline will help in the future, when in the future.

Jeff Kaditz:
It’s guaranteed.

Saul Marquez:
And so I love the approach. So is the equipment that you guys do use to run these tests unique? Is it different?

Jeff Kaditz:
The software is right now we use all off the shelf to gather the information, the analysis, no. But everything is FDA approved. We will be making probably actually within a month an announcement about some technology that we’ve actually been working on that is really the key to us being able to set up these effectively almost pop-up sites where.

Saul Marquez:
That’s what I was wondering. Access.

Jeff Kaditz:
Full Q exams. You know, I almost think of it as like a car wash for your body. Imagine if you can have these small facilities where you go in and come out and 15 minutes later and you know that you just got everything about your body measured and you’ll get a notification from your doctor if they want to talk to you.

Saul Marquez:
Mm hmm.

Jeff Kaditz:
I should also add that this paradigm of separating where clinical decisions are made from where information is gathered about a body, not as it lends itself to scale very well, it lends itself to telemedicine because you need collection sites based on population distribution. But the doctor can be anywhere. So as far as when it comes to health care accessibility, simply like if you want access to the best doctors, it doesn’t matter if you live near them anymore, if you deploy this at scale.

Saul Marquez:
Yeah, I love it. So you guys have your membership and today it’s $3495. So $3500 to get this test done and you’re in for a year. Who can access this?

Jeff Kaditz:
Anybody. I think the only people is you can’t be younger than 18 and you can’t be pregnant and honestly there’s nothing we’re doing that is that’s just the way it was written. But I don’t think there’s anything we’re doing. There’s actually there’s nothing invasive or harmful that would prevent you from doing those things. And ultimately, I think we imagine these physical exams being able to be done on anybody of any age or pregnant or not.

Saul Marquez:
Got it. And so, for instance, I’m in Chicago, so how do I do it?

Jeff Kaditz:
For now, you have to come to California.

Saul Marquez:
OK, so right now it’s in Cali, but you guys are working on a way to scale this beyond.

Jeff Kaditz:
Oh, yeah. I mean, the idea is that you can put these roughly we could put one initially. I think we could have one site per twenty-five thousand people. So I think one site we believe should be able to care for or collect information on ongoing basis for twenty five thousand people a year.

Saul Marquez:
Fascinating.

Jeff Kaditz:
So in terms of infrastructure, there are about three thousand people per gas station that gives you a sense of the scale of deployment you’d need to cover entire populations.

Saul Marquez:
Fascinating. All right. So I guess I have to get on a plane to go through this.

Jeff Kaditz:
For now. Yes,

Saul Marquez:
For now. All right. That’s fair. And folks, if you’re curious about this, definitely check them out. It’s Q.Bio. All the details are there, but obviously, it’s critical to test the status quo. And Jeff is certainly helping us do that with his approach and the approach that his team is taking. Jeff, give us a closing thought and let us know what we should be thinking about. And then the best place that the listeners could reach out to you or somebody on your team if they want to learn more.

Jeff Kaditz:
Yeah. So I think that the key here is that getting sick doesn’t have to be a surprise. I think that we have the technology today and it can be scaled to a point where we can no way, way, way in advance if we’re trending towards an issue.

Jeff Kaditz:
And I think that is the important takeaway is we don’t have to getting sick doesn’t have to be a surprise, especially when it comes to existential threats. Most diseases are exponential kind of progressions. And so there’s no reason we shouldn’t be able to characterize these trends at their earliest stages and be able to forecast how you change that trajectory. And I think that in really doing it in a personalized way is really what makes it scale. Like personalized medicine is people talk about is not the end goal. I think the reason you have to personalized medicine is to make prevention scalable because we are all unique. And if we want to account for those differences, we need to have a system that is smart enough to understand what it looks like for me to start to get sick versus what it would look like for you start to get sick. And that’s understanding your past and the baseline is so critical. As far as getting a hold of me. I’m on Twitter. My email is jeff@q.bio.

Saul Marquez:
I love it. Jeff, thank you for today. And this has been really interesting and stimulating for me and I’m certain for the listeners as well. Keep up the awesome work and excited to see you guys scale this thing out. And either I’ll be in California doing this with you or maybe you’ll beat me and get a clinic open here in Chicago. Appreciate all you’re doing, man.

Jeff Kaditz:
Great. Appreciate it.

Saul Marquez:
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