Health Tech Spotlight: Conversation on Biological Digitization & Future of Personalized Health

Q Bio CEO and Founder, Jeff Kaditz, joins co-hosts Carlo Rich and Pat Dunn on the Health Tech Spotlight podcast, where they discuss personalized health forecasting, biological digitization, the future of preventive medicine, ownership of personal health data, and more.

Transcript is lightly edited for ease of reading.

Carlo Rich: Hi, and welcome to the Health Tech Spotlight podcast. I’m your host Carlo Rich. And with me as always is Pat Dunn. Today we’re talking to Jeff Kaditz, founder and CEO of Q Bio. Nice to have you on the show, Jeff.

Jeff Kaditz: Good to be on.

Carlo: We’d love to learn more about Q Bio and what you guys do.

Jeff: We’re taking a little bit of a long view at Q Bio and starting to really think about what it means to get that check mark, that green checkmark, when you get a physical exam. And especially when we look at a lot of trends in technology, when it comes through either genetics or in biochemistry, or even anatomy, we really envision a future where the first stage of any checkup is effectively almost an analog to digital conversion process for your body. Those snapshots that are effectively taken either annually, or whatever the frequency is dependent on your risks, whether your age or genetic risks, can then be used to create forecasts. So it’s really interesting to us. If you think about the standard physical today, the most valuable question, it doesn’t really answer, which is, “am I dying?” A doctor kind of looks at you, sometimes a little blood work, they might check your reflexes, might tell you to cough, but can they say with 99% probability you don’t have a brain tumor? Can they say these things that are very existential? And so we really think that the future of the checkup is not only going to be personalized, but …the highest order bit is really, what are your existential risks? What is most likely to kill you in a year, in five years, in 10 years, based on your genetic history, your family history, or medical history, changes in your biochemistry, or changes in the structure of your body, kind of tying all those things together, we think is really the future.

Pat Dunn: That’s great. Can you tell us a little bit more about how it works? Like how would somebody get into this, Q Bio?

Jeff: Last few years, we’ve been running a prototype using all existing FDA cleared technology where in about an hour, we can measure everything about your body. So we take blood, saliva, urine, and do a whole body scan, the whole process takes an hour. When we first started, it took about four hours and, through kind of proprietary technology, but also just refining the way we were doing things, we got to under an hour. Because it’s really a throughput question. When we think about this, …if you want to give the entire population, this …level of analysis there’s automation in terms of looking at what comes out. But I think there’s also, more importantly…  automation in terms of collecting that information in a reproducible way that we really want to make, and separate measuring the human body from analyzing those measurements. Right now, if you go to a doctor’s visit, they actually conflate two steps, which is a Measurement and Analysis. And in most scientific disciplines, you decouple those things. There’s kind of the data you collect in the lab, then you go back and you analyze that data and try to fit it to a mathematical model. We kind of want to do the same thing, but just for your body. So we’ve really been focusing on just how do you collect the most important measurements, objective measurements, about the human body, and separate that from how you diagnose or analyze that data. But first, let’s make sure we’re getting the right data at the right frequency.

Pat: So what does your company look like then? Do you have a bunch of data scientists or technology?

Jeff: Data scientists, a lot of it is still engineering, we have people who have backgrounds in academia, but we’ve identified a few places specifically…that are the bottlenecks to providing almost like the Star Trek physical to everybody. A lot of people call this the executive physical. It’s a little bit of a bad name. In theory, it’s a good idea. I think that in practice, it’s expensive, it takes a long time, and that means it’s really for less than 1% of people. But the idea of, can you once a year, measure everything about a person’s body, and then look at trends and what’s changing in their body to make forecasts about them to better personalize what could be wrong with them, or what could go wrong with them. I think that’s a sound idea. And actually, I would argue that’s kind of the linchpin of the scientific method, right? Any discipline we look at that’s …been transformed from a pseudoscience to an Information Science, whether it’s weather predictions or astronomy, it started with our ability to, in a commodity way, measure changes in a system. Then later comes the mathematical models that allow us to fit those changes to forecast predictions. We think that a jump to a diagnosis based on single measurements, or population averages, is really kind of putting the cart before the horse.

Carlo: That makes total sense. So are your customers primary care physicians or health systems, or what does that look like?

Jeff: We have both. We have doctors that work with us and we have some partners that we haven’t yet announced. So healthcare systems are very interested in the ability to kind of assess existential risk, especially ondifferent timelines, which is really what preventative medicine is about. I go back to this analogy a lot, but I really think that weather and meteorology and climatology are really good examples of what the future of healthcare looks like. Meteorology really uses the same set of measurements, which is all this combination of satellite data and sensors that we have all over the surface of the planet to model and predict changes. Now, meteorology is really more of predicting changes within a week or two. So they have high precision, they don’t look out very far. Climatology has very low precision the predictions they make, but they can look out very far, like a weather model might say, the temperature in Australia is going to be 97 degrees on Friday, a climate model might say the average temperature in 100 years in Australia will be 99 degrees, I think that there’ll be a similar bifurcation in technology, once we are measuring all this information, at regular intervals about people.  A visit to the doctor will really just be refitting this kind of digital avatar that you have to making short and long term forecasts.  And there will be statistical models just like the weather, but they will be much better than we have now. And they will be personalized.

Carlo: So Jeff, my question is, in a certain context, are you a medical device company? Like what does the device look like that scans the patient? And how is that scalable?

Jeff: Well, for our prototype, and for kind of just the R&D of this, to prove out this idea we’ve been using off the shelf technology. It was really more about measuring the right things, because the thesis really was if you’re measuring the right things, and you’re measuring changes, so if you’re measuring quantitative things, and you can track changes in them, that’s much more useful, especially if you’re capturing a wide amount of information across genetics, chemistry and structure of a person’s body. Whereas a lot of diagnostics historically, make this assumption about a bell curve shape population. We’ll measure 1000 white males and cholesterol, and if you’re in the middle of that, within one standard deviation, that’s average, healthy.  If you’re above, too high, and if you’re below, it’s too low. We really think it’s actually what it means to have high cholesterol that is dependent on you and your life. There is that assumption, human health is really this very long tail distribution, which from our perspective means you need to really focus on what’s changing in an individual. If you want to make precise forecasts about what’s going on with them. We started out with, let’s just measure everything we can and start narrowing it down to what are the set of things that if you measure them together, the whole is greater than the sum of the parts? If you look across genetics, chemistry, and the structure of a person’s body, and then once we started to understand that we said, okay, well, what are the most expensive things to measure of this set of things, or one of the slowest things to measure, because if something takes 10 hours to measure, obviously, you can’t measure about everybody. Then we start developing technologies, which will be medical devices that specifically address the bottlenecks in terms of cost or speed to take those measurements. Because again, our vision is a world where when you go to get checked up, it’s blood, saliva, urine, whole body scan, in 15-20 minutes, you go home, and you get a notification if your doctor wants to talk to you, otherwise, you’re good till your next checkup.

Carlo: That makes sense, that sounds amazing, I noticed that you have a lot of interest. And you’ve explored a lot of different fields, from rockets to consumer electronics to finance, how did you get into healthcare?

Jeff: I think mainly because when I was in school, biology was always very interesting to me. But back in the early 2000’s, computers were a lot slower for one, and we also didn’t have the sensor technology that we have today. And so things like biology, and even psychology, neuroscience is kind of changing…. A lot of modern technology that we have is changing biology, because for the first time in human history, we can actually kind of digitize biological system state. And by that, I mean we can measure it, rather than we can observe some qualitative property of it, we can actually measure its state. And then if you can do that in a reproducible way, that means you can measure changes in it. So rather than observing biology, in a petri dish, or in a zoo, we’re starting to actually be able to make it quantitative. That combined with massive leaps forward in computation, computational biology, we can now start to model certain biological processes. And I think a perfect example of this is drug discovery, clearly is going to be computational. Rather than having to synthesize potential pharmaceuticals. You try and synthesize 100 things and you see what works, to try and find a million things, and then identify the 10 best candidates to actually physically synthesize. That’s much cheaper. Atoms are way more expensive to move and push around than bits. …So honestly, this is true in every discipline, every part of the economy, the more we can do up front, digitally, to kind of whittle down the search space of solutions. Before we go to atoms, the faster, cheaper everything gets.

Carlo: Yeah, that makes total sense.

Pat: Yeah, this makes total sense, and it’s also quite deep. So how did you get to this space?

Jeff: I was always very interested in biology… When I was young, I was really into astrophysics. And at some point, it occurred to me, we know more about what’s going on outside of our bodies in the universe than we do inside. So it’s like there’s this whole universe inside of our bodies that we really have barely understood compared to how well we understood the rest of the cosmos. So I think that alone makes it very interesting. And this isn’t unique to me, I’ve had some frustrations with the healthcare system, and coming from a background and high energy physics, computer science, when you are asking a healthcare professional, what’s wrong, and they give you kind of ideas, but it’s not based on measurements. I would say, well, there has to be some kind of experiment, you can do some set of measurements we can do over time to kind of isolate what the problem could be. And their response was always, well, that’d be expensive, or we don’t have the technology to do that. And that was very frustrating to me, because literally, the way we study, every natural system in the universe is the same. We measure how that system is changing, and we try to model its changes. So we can predict the next measurement. For some reason we don’t treat the human body like the rest of the universe. And I think that there’s a lot of dogma in healthcare, that the human body is special, but it has to adhere to the same laws that the rest of the universe does. If we had these systems and methods for studying the universe outside of our bodies, why don’t we apply those same methodologies to studying what’s going on inside of our bodies? That was really what kind of motivated me to pursue this.

Carlo: That’s really great. I noticed that you’re a serial entrepreneur, you’ve been a co-founder before, most notably at Affirm. What lessons or best practices from those previous experiences have helped you in this new venture?

Jeff: Everything I’ve done has been in such a different space. This isn’t super deep, but I think it’s really the successful companies, really the quality of the initial team, and how well that group of people works together. It doesn’t matter how good individually a set of people are, it doesn’t matter how good an idea is, or how big an opportunity is, what at the end of the day, what matters is if you have a tight knit group of people that are willing to do whatever it takes, and kind of suspend disbelief. Because if you’re joining a startup, you’re betting that you can overcome the odds. If you’re doing something like what we’re doing, you not only have to overcome scientific and technical obstacles, there’s also market obstacles. Affirm, there were no real technology obstacles. I mean, you can say we had to solve some technology problems, and there were some algorithms. But when we set out to start Affirm, we were never like, oh, the laws of physics are going to get in our way here.

Carlo: Yeah.

Jeff: Right. That was never a concern. It’s software. Now, it was really at the end of the day, it’s more of a business, a market, a product, a sales problem. Depending on where you are in health tech, you first have to solve potentially an unsolved problem. And then you have to figure out how that fits into a market. And that’s not always the case. But I think that that is at least twice as hard. In some ways.

Carlo: That’s great advice.

Pat: So what are you looking forward to when it comes to health technology?

Jeff: I think one of the things I’m really looking forward to is, and this is pretty basic, but number one would just be a world where all of us have immediate access to all of our medical records, and we can share them with anybody we want instantaneously. I could do that with the rest of my digital information now from my phone. Why can’t I do that? Why can’t I get a second opinion from a doctor in India just by sharing a link with him in the same way I can share all my music with somebody. So I think ownership of this data and the ability to take it with you. So there isn’t this vendor lock-in that is really by design in the healthcare system. I think that, more than anything, this will have the biggest impact. Of course, everything after that, in some ways, is incremental. Because after that, it’s just can we measure more? Can we measure it better? Do we have better analytics for analyzing that data? But until you have that, to me, that’s kind of the baseline for us having control over our health is the information about our bodies.

Carlo: I totally agree and breaking down silos is definitely a challenge. And there’s obviously debate whether the patient owns their own data which fall on the side that you do. I think the patient should be able to use their data however they want to. As you may have heard from our previous podcast, we like to focus on the people of health tech, so not just the amazing things that you’re working on, but more about you. What are something that our listeners might not know about you or a hobby or something that you like to do outside of your day job and health tech?

Jeff: I’m a pretty avid biker and skier and living in the Tetons, I get to spend a lot of time exploring those mountains, which I love.

Carlo: Is that bicycle?

Jeff: Mostly mountain bike, I used to do some road biking, but I don’t know if that’s public knowledge or not.

Carlo: Great. How did you end up living in the Tetons?

Jeff: I came here in 2012 for the first time in December, and decided then that this is where I ultimately wanted to end up. And then obviously, with the pandemic. I think for the last 20 years, I’ve tried to figure out how I could live in a mountain town and also work in high tech. And I guess the answer is a pandemic. But honestly, I think the biggest struggle for me was always wanting to live in relatively remote places that didn’t really necessarily have the professional interests that I wanted. So that was the dichotomy.

Pat: That’s great. So how can our audience connect with you? And with Q Bio?

Jeff: You can check out our website, we’ll be making some… exciting announcements Q1 this year around funding and partners and some products have been in development for a while that we’re just getting ready to start talking about and you can always email me at Jeff at, too.

Carlo: Thank you so much for joining the show, Jeff. We really enjoyed our conversation and look forward to following Q Bio and amazing things you guys are going to do.