As technologies advance, the experiences of our lives are becoming ever more personalized. Online stores now offer up must-have items tailored to your tastes upon login, and streaming services quickly filter through thousands of options to offer up the perfect suggestion for your next binge, based on your personal interests and habits. Universal solutions, in many ways, are a thing of the past. So why are they still the norm when it comes to the most important aspect of your life — your health?
One-size-fits-all solutions are on the decline in many ways, perhaps most importantly, on matters of personal health. Blissful ignorance, once an excuse for poor dietary habits or a sedentary lifestyle, is no longer possible as messages about the importance of a quality diet, exercise, and stress relief infiltrate our daily lives. The very best recommendations are, of course, driven by data. And while our cultural norms have helped shift so many unhealthy behaviors we once considered perfectly acceptable (smoking, for instance), it hasn’t yet become mainstream to consider our individual risk factors and how our unique DNA, medical histories, vitals, and more play an incontestable role in health outcomes, disease risk, and reactions to various treatment modalities. That’s where precision health comes in.
What is precision health?
The healthcare system as we know it is essentially stuck in the dark ages when compared to the abundance of customized and curated experiences we enjoy daily. . The reason? Medicine as we know it generally favors reactive solutions versus proactive prevention and risk management. Despite the fact that many doctors very much want to practice preventive medicine, the traditional system is designed to treat illness and doesn’t really equip practitioners with the proper tools to fully deploy it in practice settings. On the research side, scientists may study diseases and cures, and not all the clinical signs, symptoms, and preventive strategies associated with those ailments and treatments. This standard of reactive healthcare — treating an illness once it’s already wreaking havoc on your body — simply doesn’t make sense, given the wealth of data we now know how to leverage in order to make better informed health decisions.
Precision health focuses on predicting, preventing, and precisely treating disease, and the ultimate goal is to create completely personalized health strategies that align with and support every individual’s genetic, biological, and chemical differences. Precision health utilizes data to deliver the right tools and interventions at the right time to the right person — a seemingly simple concept that’s historically been unattainable due to the blanket, reactive approach of traditional medicine.
By compiling information on genetics, lifestyle, environment, and specific biomarkers from a large sample of volunteers, this approach has the capacity to redefine what “healthy” truly looks like and identify early warning signs of potential problems. While traditional research studies typically examine participants with a shared disease or biological abnormality, precision health looks to track specific data in a generally healthy population, which, over time, can reveal clues about certain deviations.A sustained change in heart rate or blood sugar, for instance, could indicate an impending disease. The earlier these issues are detected, the sooner treatments can be implemented, so why not find out what’s going on in your body before these risks turn into big problems?. What’s more,people who choose to monitor their biomarkers may be able to initiate prevention strategies that stave off diseases altogether.
How tracking your own body over time can influence your health
Tracking large populations is one important piece of precision health and tracking your own body puts that into practice. Here’s where panomics comes into play. A systems biology approach integrating the study of genes, proteins, metabolites, and more, panomics involves the use of advanced, non-invasive imaging, chemistry, vitals, and other tools to build a comprehensive snapshot of your health. When these snapshots are tracked over time, the data paints a clear picture of changes that indicate the need for immediate or future action — whether that means modifying your diet, a course of medication, more sleep, or some other form of intervention.
Q Bio co-founder, Michael Snyder, PhD has been tracking his personal health data for many years using readily available technologies, and tells the story of one clear example. While traveling, Snyder detected a curious change in his heart rate and blood oxygen level. He didn’t feel sick, but a fever followed, and the combination of changes happening in his body at the same time made it clear to him that he’d somehow contracted Lyme disease. Not being anywhere near where Lyme is commonly found, his own physician thought the concept was highly unlikely, but with the data as proof in hand, Snyder insisted, and started a round of antibiotics.
Because of his experience, interest in, and familiarity with panomics, Snyder was able to observe and analyze his own data to discern observe subtle clues about his health status — an intensely personalized point of view the average person wouldn’t have access to via traditional medicine. By the time the test came back and confirmed the diagnosis, he was already finished with a round of antibiotics and the disease was eradicated from his system. Seeing the warning signs in real time allowed him to take action long before the typical healthcare process would have even detected an issue, if ever.
Why precision health matters now
We’re living in an increasingly customizable world that’s advancing and growing more technologically sophisticated every single day (just ask any one of the artificially intelligent personal assistants that know your stereo volume and living room lighting preferences!). But until now, we haven’t fully applied the power of existing technologies to better our health and wellness. We’ve finally arrived at an exciting time in which we can use these timely, targeted, cost-effective prevention and treatment strategies and become empowered to make the best possible choices on a regular basis. Working with experts who can help us make sense of our body’s signs and signals is the future of healthcare, and it’s quickly becoming the modern reality as the most accurate, affordable, actionable way to optimize our well-being, reach our goals, and remain healthy for as long as possible.
In this podcast recorded by Andreessen Horowitz “a16z” in June 2020, experts discuss what a new operation system for preventive health looks like.
Our Founder/CEO Jeff Kaditz, joins a16z General Partner Julie Yoo, Senior Editor Hanne Tidnam, and physician entrepreneur Ivor Horn, a primary care pediatrician for more than 20 years, in a podcast conversation. As a16z introduces, primary care was meant to be the front door to the healthcare system, but in some ways never set up for success to begin with. We need a new operating system for primary care—one with a different, deeper understanding of the patient, the context of their world around them, and the processes we have in place to figure out who sees a doctor and when, to use the system most efficiently.
Transcript as follows (lightly edited for readability and clarity in places):
Hanne Tidnam: Hi and welcome to the a16z podcast. I’m Hannah. Primary care was meant to be the front door to the healthcare system, but in some ways it was never set up for success to begin with. We need a new operating system for primary care, one with a different deeper understanding of the patient, the context of their world around them, and the data and processes we have in place to figure out who sees a doctor and when to use the whole healthcare system most efficiently. In this episode of the a16z podcast we talk about what the primary care of the future should actually look like. Joining us for the conversation, our a16z general partner, Julie Yoo, physician entrepreneur, Dr. Ivor Horn, a primary care pediatrician for more than 20 years, and Jeff Kaditz, CEO and founder of Q Bio, a platform that identifies and monitors each individual’s biggest health risks. We’ve been seeing COVID and the coronavirus put enormous pressure on the entire healthcare system. So, let’s talk about what the effect of that has had on primary care. Where have we seen primary care kind of succeed in this moment? or has it? or where have we seen it fail? What is it or what are we learning about the cracks in primary care from from this particular moment?
Dr. Ivor Horn: We all remember the primary care of older times when it was our doctor in our community and that doctor knew about that community and had the trust of the community. And one of the fundamental things and foundations of that primary care was that experience with trust and being able to share information with that provider. I think some of the things that have been helpful about primary care is the fact that there is that level of trust. Yet, that’s also where things broke down because people ran to the place in the space where there were limited resources and overwhelmed that area. And there weren’t the opportunities to use other mechanisms, such as telemedicine or telephones, to communicate with people and to do the triaging that needed to happen rather than people being exposed, even in the doctor’s office.
Julie Yoo: Yeah, it is what we call low acuity entry point for care, whether it’s a stuffy nose or a rash or you know something very basic, a patient can get a very quick evaluation and not have to necessarily see a higher-end specialist or go to a hospital or some other more expensive and more complex type of care setting, and essentially get their needs taken care of in the most cost effective way possible. Primary Care was really meant to be the front door to the healthcare system. The unfortunate irony of the current situation of primary care was that it was already at almost a crisis level with regards to access. Your ability to actually get an appointment with a primary care doctor, despite the fact that that is actually the most appropriate entry point, would sometimes take months, right?
Jeff Kaditz: There’s just a very fundamental economic fact which is the most scarce resource we have in healthcare is doctor’s time. Doctors are extremely expensive to make. And not to mention the fact that the ratio of GPs per capita globally is going down. And so if their time isn’t used effectively, that’s the most wasteful thing we can do in healthcare. This whole flattening the curve, just in general, primary care should be about flattening the curve. The learning curve is really about not overwhelming resources and how do you then, if you’re not trying to overwhelm resources, how do you prioritize those resources. Well, people who need care sooner should get it first. What this is exposing is not just our ability to potentially effectively triage and segment risk in a population quickly, so that we can prioritize who gets attention, based on need and priority. And what we really need to figure out is how do you know on a serious basis who’s at the highest risk. Who do they need to spend time with in order to really focus their care. Because if we can pick out the one person who needs to see a doctor in any given year, out of 10, that means a doctor could effectively care for 10 times as many people.
Ivor: The other thing is, all of the people that are around the doctor that also provide support to patients that we haven’t actually utilized effectively. Whether it’s the nurse or the front office staff person or, especially community health workers who know the context in which people live, to actually do some of that early stage understanding of who really needs to see the doctor, and how you can communicate with them on a more regular basis, such that when they do need to see the doctor, they actually are coming in. And that time is of use and used appropriately and well.
Hanne: So at the moment this sort of triaging is done in the most inefficient klutziest way where people are literally left in a giant vacuum of trying to get on a telephone queue and describe some vague symptoms that one person may describe in a completely different way. You’re talking about a different kind of support and information gathering for that type of triage. So let’s talk about what that could look like.
Jeff: Traditionally in medicine you measure something if you want to diagnose something. And I think that that we have to move away from that notion. We should think of measuring information as health monitoring, not looking for illness. That’s how we’re going to get to much more sensitive diagnostics is thinking about when we see patterns or accelerations of changes across multiple variables. But to embrace that we have to stop thinking of screening for disease, versus monitoring health. I think the way to think about it is a spectrum. There’s kind of low fidelity, high frequency data. And then there’s high fidelity, low frequency data. And there’s lots of information in between. When actually information needs to be gathered from a person that requires a physical visit, does an actual doctor need to be there? Or can that information be gathered very effectively if it’s available when the doctor actually has a conversation, whether it’s in person or remote? In theory, no doctor should meet with the person unless they required intervention. And if the system was really optimal, that’s what would happen.
Hanne: Can you give an example of what that looks like?
Jeff: Well I think I think it’s different levels of triage. I think in theory you could be monitoring somebody at home, and based on changes in risk, — say we think you need to get a lipid panel done, — and then based on that liver panel say we’re going to notify this doctor that you should schedule a time to talk to them and automatically connect them in the next week. But you can also imagine an intelligent scheduling system that went into this, that would actually prioritize a doctor’s schedule based on need. It’s kind of tragic if a person is going in for just a general checkup to say how they’re doing, — like an 18 year old healthy person with no health risks, — and takes time from a person who is having severe chest pain, and has a lot of indicators. They really should talk to a doctor. We think there’s just fundamentally a missing layer to primary care, which is this automatic data collection layer, which automatically determines what is the right set of things to monitor about an individual, and then can alert an individual and a doctor, when a doctor’s time is required to intervene and have a discussion.
Ivor: It’s really important for when we’re thinking about the tools recognizing that primary care has to be able to not understand that information in the silos, but along and across the care continuum, and how do providers begin to connect that data and prioritize that information in how they support and provide care. People are not entering into the health care system at one place. They may be entering into the health care system at an urgent care clinic or via telemedicine or via a specialist for that matter.
Julie: Yeah, I think you’re highlighting that it’s not just the information chasm that leads to all these challenges, it’s also the logistics challenge as well. And you know we think a lot about the movement of healthcare into the home. The fact that you have to go to your doctor to even determine that you need a certain lab test, and then you have to wait for the lab test to be done to come back again to your doctor to actually interpret those results, and then get your care plan. You hear all the time about patients deteriorating in that window of time when they’re waiting for those things to happen. When, had you done that test upfront before they came in for their first visit, you may have been able to act on that sooner. And you see the same thing on the flip side where after you discharge patients from let’s say a hospital or other acute care setting. Let’s say you’re a heart failure patient, generally speaking, you’ll want to set that patient up with check-ins after they leave the hospital. Many of them end up actually getting readmitted into the hospital because they don’t get the care that they need.
Hanne: What is it that’s so hard about just flipping that one simple thing? Why would that be? What is it about the system and the way it’s set up that would make it so hard to just flip that?
Jeff: There’s a general problem that we’re talking about, which is overload. That’s why flipping a switch is hard because there’s a whole new class of clinical decision support tools that need to be there. Otherwise, you’re actually creating more work for a doctor. If you measure a thousand things about every person and a doctor is supposed to look through those things, that’s not reasonable. So you need to have intelligent tools that can actually highlight the key things.
Julie: It flips the whole paradigm on its head because the current system is that the patient has to determine whether or not he or she needs to go see a doctor versus, shouldn’t it be the doctor who actually knows when to reach out to you?
Ivor: One of the things that we also need to consider is the context of that data. Understanding the context and the environment in which people live. And what that data means in the context of their life. You may have someone who has a cardiac condition and has a cardiac treatment, and not having the context of the fact that there’s no one in their home, there’s no one to actually acknowledge to them that they’re having a change in their status, to say you’re not breathing correctly, you need to call in. If we do or do not have that data, following them in that short period of time, it matters in how we triage that data and how we bring that data forward to the provider. We have the capacity to bring information and data forward to providers in a way that prioritizes not just based on what the lab test shows and what the trend of the lab is, but also some of those social factors and those behavioral factors in context. Is this person not moving as much as they typically would? How do we take that into consideration in that dashboard that a provider gets? We all know that there’s bias in data. We know that people have not collected race, ethnicity, or language preference data. And how we interpret that data. And what what comes up in that algorithm or what comes forward in that, that clinical decision support tool. And it’s really important for us to not run away from those biases and ignore them or say they don’t exist but run to it, identify it, correct it. Make the changes that we need to make. Ask the questions that we need to be asking. So that as we’re moving forward, we’re actually improving things and making them better. That we’re including the communities that are impacted by these biases as we’re building. And while we’re building, getting their input along the way, to make sure that what we create is for everyone and creating more equity as opposed to more inequities in care.
Jeff: That’s a huge part. I think the context is so important to determine whether or not a measurement or a trend is significant. We’ve spent a ton of time figuring out how we weigh the significance of measurements, based on genetics, lifestyle, medical history. I think the right way to think about it honestly is you can call it an OS, or even an analytics platform for the body. Again, where the goal of the system is to monitor what’s changing. And so by the time a doctor sees a person, they actually understand and have all of this in context, and have the tools to understand where this person lives, how is this person like other people where they live, other problems people have had in that area.
Julie: One of the paths to overcoming these challenges that you’re describing is actually to think beyond the electronic health record because I think so much of the bias that does exist today is that we’re relying on these highly structured, very sporadic, — as Jeff, you said earlier, — the low frequency, high fidelity data points. That’s pretty much solely what we depend on today in traditional medicine and traditional primary care. Whereas, the vast majority of insights that probably determine both your current state as well as what your progress is going to look like over the course of time, comes from everything. All those social determinants and behavioral and demographic related information. And part of the challenge of why we have so much bias, and why it’s so hard to overcome that, is that we haven’t collected that data historically. Just the notion of longitudinal data between physician encounters that is completely unaccounted for in traditional medical record systems. Even when you look at these chat bots that are popping up everywhere to help us triage whether or not we need to go see someone for COVID related issues, none of those questions are being asked. And so I think that’s one of the huge opportunities here is to really open up the aperture on the nature of data that’s being collected.
Jeff: I mean, if you think about it, EMRs are really designed to administer a bill. And most information we have in EHRs is biased towards sick people. They’re biased towards people who have access to care. And when we talk about a healthcare system that gets better, unless we can decouple measuring the human body from care decisions, which are opinions at the end of the day, and physician predictions, we will never actually close that feedback loop. Because we can’t look back retrospectively and say, okay, could we have, knowing what we know now, come to a different opinion. If you’re just capturing the opinion, not the inputs to the opinion, you can’t actually go back and learn. One of the interesting things that you’re talking about Julie is, if you take a step back and think about a person that goes out interacts with their environment almost as a sensor. I actually see the future of healthcare being able to prevent things like Flint, Michigan. If you were actually monitoring the population, and clinicians had access to information, you’d see a change in population health as soon as those waterpipes were switched, not two years later when it was damaging kids neurological systems.
Ivor: Understanding all of those social determinants of health, one of the things that we’ve learned as part of this process is that the context in which people live, learn, work, play, pray, can’t be bucketed into just housing, or just food insecurity. It has to do with the context of the number of people in your home, the needs of those people in your home, what your job is, and the requirements of your job, and the limitations of what you can and cannot do for your job. All of those things impact the data that needs to come forward. When we talk about social determinants of health, we often talk about the negative consequences of social determinants of health. Yet we don’t often talk about the fact that people may have a community in a social network that impacts their ability to get support that we didn’t understand or that we didn’t tap into. We didn’t think about the level of resilience that a person has and what are the things that influence a person to actually do more in terms of their exercise or the way that they’re eating. That should come into play with the provider being able to give more effective and more useful guidance to that person when they come in, when they’ve been triaged accordingly.
Hanne: So other levers you can pull besides a prescription, besides a diagnostic test, besides an office visit. Communities and support.
Ivor: Exactly. And some of those things can be done via telemedicine. We often think about it as this one-on-one video perspective, but there’s a lot that you see in a telemedicine visit that’s around a person that gives you context. The other tool is the simple use of a telephone conversation, and using that as a tool for checking in, and that being an important factor in creating more longitudinal data. The value of longitudinal data is so important and we don’t take that into consideration. We piecemeal it together, as you said, in those low frequency, high fidelity, EMR type visits. But we have more frequent steps now that actually broaden our understanding of a patient in ways that we never could do before.
Jeff: I actually think the key to personalized medicine is really in the ability to figure out what are the most important things to track about each individual based on their risks, based on this person’s genetics, medical history. What is the subset that actually needs to be monitored about this person and the frequency. And all this telemetry is just connected. That first order triage or the collection of data should almost happen passively without a doctor having to worry about if the right things are getting measured. So when the time comes and a person, let’s say, has to be rushed to the ER or they start to have symptoms, a doctor has all the context that they need. Right now, if you get rushed to the doctor, the doctor starts with almost nothing in the ER, and it becomes an information gathering journey before any decision can be made.
Hanne: I hear such a tsunami of new types of data available that can be incredibly valuable, but aren’t being used the way they should. And major shifts with the entire orientation of the system. What is the sort of management process and pipes that need to be built to make this vision closer to reality?
Julie: Today, we only measure the things that are diagnostic in nature, and part of the reason why is that those are the things that get reimbursed. And so I think that’s a huge part of the answer to this question is how do we not just create the pipes, but how do we actually make the cost effectiveness argument that measuring that data actually has enough clinical utility that makes sense to pay for it. Part of why we’re in this challenging spot is the fact that we are reliant on a system that only pays for individual tasks. And therefore, it didn’t make sense from a payer perspective to reimburse for a million things to be done. It only made sense to reimburse for the things that you know really mattered and really move the needle. Whereas in the value-based care world, they are able to innovate in unique ways to take advantage of new data sources to engage with patients in ways that wouldn’t even fall into the definition of clinical medicine 10 years ago, but are now absolutely the direction that primary care in particular is headed. We see that in light of programs like the primary care direct contracting program with CMS, and more and more ACOs getting traction with even commercial payers, etc.
Ivor: You’ve got to realize that, really, a little over one in nine people actually have enough health literacy to understand how to manage their healthcare and manage the health care system. So the ability to communicate and translate that information into a way that people can effectively provide and support themselves in their care journey [is incredibly important]. Because the majority of their care journey will happen outside of the four walls of any healthcare system. And any information that we can get that allows them to do that effectively means that they’re going to have better outcomes, means that they’re going to have better quality of life, and means that they’re going to have better quality care. And so understanding those fundamentals of how we use data across that care journey is really important. As a primary care provider, the onslaught of information that we have from wearables, from our mobile phones that tell us how people are moving, can be overwhelming if it’s given all in one place, and not with any context, or with any prioritization. And I think that’s the journey that we’re on when we start looking at why it’s important for us to get this data. And it’s important for us to understand this data in context of what we do. And there’s the data for the primary care provider and there’s the data for the person.
Julie: And I think that highlights the fact that patients are not actually an end user. That’s a consideration when it comes to traditional clinical tools. I was a patient of a specific hospital when I lived in Boston. And it turned out when I was admitted for labor and delivery, I had multiple records in their systems based on different instances where I had different needs. We’re describing primary care, and the responsibility of this notion of a PCP knowing everything about me, when that can be, number one extremely overwhelming to know. Every single part of my healthcare journey may have very different needs: if I’m pregnant and going through a maternity journey, versus if I get sick with COVID. The type of information and the type of judgment that’s necessary in each of those instances is very different. How do you appropriately balance the horizontal view and the longitudinal journey of a given individual with the notion of the bundles of care and the unbundling of primary care across the different mini journeys that we all have as patients. The type of data that I need for journey one versus journey two can be very different. If the cost of measuring everything is low enough, such that I can collect all that information, perhaps that’s the best way to go. But how do I then appropriately overlay the right semantics and the right context for that particular instance of care need.
Jeff: There’s a lot of times where doctors are forced, when time is of the essence, to make decisions based on partial information to be safe. And I think that if they had the context of a person’s entire history and what’s changed, there’s a lot of things that they might associate with an immediate symptom that are actually normal for that person. You know we’re all used to tools like Shazam now, but trying to figure out what’s wrong with a person based on a single measurement, or even a set of measurements at a point in time, is a lot like trying to identify a song based on a single note in that song. It’s just not possible. A lot of songs share the same notes. You need to hear a sequence of notes for it to actually be a song. And similarly, I think you need a sequence of measurements to actually understand the story that’s going on in person’s physiology and kind of can explain where they are.
Hanne: You need to hear the whole song to know what it’s saying.
Ivor: I love your Shazam analogy. One of the things that I think is really interesting about Shazam is that if there’s a song in there that hasn’t been played enough, you can play that song and Shazam won’t pick it up. I think that’s the same thing that’s true with data, and whether we’re collecting data from all all the people that we need to be collecting data from. Because if we don’t have that information, we’re not going to be able to recognize that song. And I think we need to make sure that we’re including folks so that we can recognize that song in everyone as we’re as we’re making these transformations in healthcare. I think it’s a really awesome opportunity that we run to, instead of running from. The other piece is around, when we give people information, what is their ability to make those changes. It’s also impacted by the environment and the priorities and the access that they have, whether it’s the ability to exercise, or have healthy foods, or what their job requires for them to do, or the ability to move around in their neighborhood safely. And so I think us thinking about that in the context of how we can impact and help people on all levels, once we have the data, is really important.
Jeff: Yeah, I totally agree. This information is so valuable for us just optimizing our society. That’s, I think, ultimately how we get to a health care system that actually gets better, where every generation is healthier than the last because we understand better how to care for each other. What we’ve started to see is that when you give people information, feedback, they can very quickly and intuitively correlate changes in their behavior to improvements in their health, or decrease risks. But they don’t have that feedback right now.
Julie: It also begs the question of what is the primary care provider’s skill set, what are those skills that need to be in the future. I mean we’re almost uppending the very definition of what is a PCP. It’s no longer just about interpreting the test results, or doing your basic workup, but really it’s about how do you ask the right questions of the data. It’s almost like the wave of data science that occurred in general engineering and computer science, where the skill set became less about how do I write really good code, but more about, now that we have so much data, how do you best interpret that data and build the tools. You can imagine another credentialed provider type that has to exist to make all this work, and what happens to the traditional physician. The archetype of the person who’s doing the real clinical interpretation, does that continue to exist? But in a way that only has to focus on the sort of the things that get escalated to that human who actually requires some judgment, to be able to look holistically at that patient in that context with all the information, etc. And then do you have a separate class or tier of folks who are standard in clinical practice who are the dataists that support that physician.
Jeff: If we do that, we have failed to build the right tools. Technology should not require people that get a data science degree. These tools should liberate a doctor to actually make just decisions. I assume everybody on this call remembers going to the library and using the Dewey Decimal System. Obviously that wasn’t going to work for the internet. How long did it take you to learn to use Google? I think actually that a clinical decision support of the future liberates a doctor to just ask questions and the system will give answers. The doctor will say, tell me about just the respiratory system and the system will just summarize that. The tools might require data scientists to build, but there should not be cognitive burden on a doctor to actually use those tools, any more than I should have to have a degree in statistics to be able to search the internet.
Ivor: Yes, it will absolutely optimize what we do and help us to do things better and faster and more effectively so that providers are not burnt out by the overwhelming information that they get. And there has to be an integration for the opportunity to let that human-to-human interaction inform the information that’s in front of them. Our ability to gather and collect data now is phenomenal. And it’s wrought with biases that we have to recognize and understand. Those biases impacting in the decision support for a provider are significant in the outcomes for a patient. There needs to be more understanding of how to analyze data by providers. The lack of ability to understand how data can be transformed to tell whatever story we want it to tell is becoming quite apparent to us right now. The ability to understand how to not just look at a lab result and say okay it’s within the normal range, or it’s not within the normal range, is no longer going to be acceptable.
Hanne: So, primary care, 5 to 10 years down the road, does that just mean it’s all around us all the time, like there is no primary care, it’s just everywhere care. What does that shift look like at the farthest end of the spectrum?
Julie: Yeah, I think, there are a couple dimensions that change. One, the notion of resource constraint that we started with. I think that will look completely different in the future when we are able to tap into the nationwide, or even global network, of PCPs through virtual care, through telehealth, in a way that is reimbursed, in a way that takes licensure sort of burdens off the table. So the notion that I have to rely on the supply within a five mile radius of my home, such that I can get the care I need, kind of goes out the window. I think that’s one thing. And then I think the other thing is flipping the paradigm from one in which we as the consumers and the patients are the ones who have the burden today of figuring out whether or not we need to get care to one in which the system, because we can be proactive about identifying signal in that data that says, “Julie, you’re the one who needs to come in now,” versus “Honey, you’re fine and you can stay home for the next six months.” I think that whole paradigm will flip such that we wait for the doctor to tell us what we need, versus us having to put ourselves in the queue, to figure out whether or not we need to come in.
Jeff: I think that primary care doctors, the role if anything is amplified. They’re the QB of your health. They’re quarterbacking. They’re the director. They’re calling the plays. They just have a lot more data at their disposal and tools that help them understand what the most important parts of that data is, so they can ignore noise.
Ivor: A primary care provider may be the quarterback, but what the coaches look like are very different. The coaches may be community health workers. They may be family members. They’re definitely going to be the patient themselves — they’re going to be the head coach. You’re also going to have other resources like wearables and smartphones that are part of your defense and part of your offense that are also playing as part of the team, and recognizing that it’s a team sport.
Hanne: That’s awesome. Thank you guys so much for joining us on the a16z podcast and thanks especially to all the primary care doctors being all our quarterbacks right now.
One of our core values here at Q Bio is Interdisciplinary Respect. It is our specific way of putting company diversity and inclusion at the core of how we want to operate and grow. We believe that our mission — to empower everyone to better understand the most relevant changes in their bodies, so that they can take control of their health, — does not only benefit from, but absolutely requires multiple disciplines, points of views, and experiences to build.
Our employees come from all walks and stages of life. We have people starting their first job here at Q Bio to start-up veterans. We have an active #kidsnpets Slack channel and welcomed 7 new babies to the Q Bio family in this past year alone(!)… and one new fur baby. We have employees who are the first in their families to receive a college education, others who have come straight from vocational training, and folks with multiple graduate degrees and doctorates.
Looking at our current company statistics, we’re also proud to have an early diverse team:
41% of our employees are female
25% of our engineering team are female
63% of our employees are first or second generation immigrants
7% of our employees/full-time contractors are international
41% of our employees are non-white / non-Caucasian; we don’t have any employees who are Black, but we have Asian and Latinx representation
And while we don’t collect information on gender identity and sexual orientation, we know that there is representation within the company as well
While our company value to have Interdisciplinary Respect is not explicitly about diversity, it reflects our take on what diversity and inclusion means at the core of our company. Together with our other two values to Earn Trust, and put Mission First, we want to continue to grow our team to have compassion and respect and to best reflect the type of community we want to see reflected back in the world.
My ten-year-old self would not have guessed that I would be working as a software engineer to build the “physical of the future.” The palm trees outside my apartment would come as a surprise, too. I grew up near Chicago, Illinois. I went to college at the University of Michigan, where I intended to study biomedical engineering. My passion for coding was discovered while completing a first year computer science requirement for engineering. Since the switch, I have had the opportunity to spend a summer at Epic Systems, where I worked on software to auto-fill medical record forms from doctors’ dictations. I was also able to work on a hybrid and edge cloud solution at Microsoft, called Azure Stack. After almost 4 years of working in enterprise software, I was looking for an opportunity to make a more tangible impact on everyday people.
In searching for my next step, it was important that I found a place that brought people of different expertise together. If it had that, I knew there would be more than enough learning and challenging problems to solve. It was also important that I believed the company was going to make a real difference and do so for the right reasons. At Q Bio, the team includes people with backgrounds in MRI, physics, biology, chemistry, genomics, bioinformatics, business, and, of course computer science, too. There is #InterdisciplinaryRespect (one of the company’s early core values). And the technical problems are challenging. Capturing, processing, securing, and querying the most relevant data about a person’s health is no easy feat. On top of that, Q Bio is working to truly change the way people understand their health, which is a great reason to come (or log in) to work each day!
It is refreshing to return to the healthcare space, especially at such a critical time. There is a lot to learn in this field, but it is exciting to see that computing is the glue that holds all of this information together. If we gather more of the right data at the right times, we can detect meaningful change in the body, enabling medicine to be tailored to the individual. I cannot think of a better way to spend my time as an engineer. It’s an exciting future ahead and I’m glad to be a part of it at Q Bio!
Every start-up goes through many twists and turns in growing and building a new business from the ground up. Throughout, our underlying core values help steer us even if we don’t have an exact map for the mountain we are trying to scale. As a small company, we wanted to have a starting document with values that feel organic to us and reflect the company at this time. We believe these are values that will grow with us as we grow.
Our mission is to empower people with a deeper understanding of their body and how it’s changing, so they have more control over their health.
Our long-term vision is that when preventive health is available to everyone, treatable diseases will no longer take lives.
We hope to share more of our working expectations and principles in later blog posts, but today we wanted to share our starting core values here at Q Bio.
We are on a mission that can help redefine preventive healthcare. Many companies talk about the importance of being mission-driven. It’s all the more important to us given our mission is incredibly long-term and we often won’t have the answers on this long journey. Our goals as a team are bigger than any of us and we can (literally!) change lives. We will always be forward-looking and put our mission first.
What we are building requires people with skills and backgrounds in a wide variety of fields with different professional experiences, from different walks of life. If we want to fully leverage this diversity, we need cross-pollination of ideas, open discussion, and sharing of knowledge. We enjoy and approach one another with compassion, kindness, awareness of diverse backgrounds and fields of expertise, and with an expectation of greatness.
We earn the trust of our members, our partners, and our teammates every day. This means we listen, speak, and treat each other and our members and partners with respect. Even more so when conversations are hard or awkward. We actively seek to earn trust.
Please help us hold true to these early Q core values. We hope to have a chance to share our values in action!
I am not into tech! I don’t own fancy and modern technological gadgets. I am not dreaming about buying the next futuristic car. I am not interested in space missions, nor do I feel excitement about the possibility of going to another planet. As a matter of fact, I am too scared to ride a rollercoaster — how can I possibly dream of jumping into a spaceship and going to Mars? So what brings me to the Silicon Valley, the center of the high-tech world?
Despite my high school curriculum being mostly in classical subjects such as philosophy, ancient Latin, and Greek literature, my career has been very focused on science. In hindsight, the honest reason I took this path is because I did well in math and physics with minimum effort. Like many kids, I decided that I liked doing the things I was best at. Fast forward a few years, I found myself with a Ph.D. in electrical engineering, specifically in numerical modeling which is the field of crafting computer codes in order to find approximate solutions of very complicated (and often otherwise unsolvable) physical problems. I was especially interested in computational electromagnetics, the discipline that aims to find numerical approximations of Maxwell’s equations. As you can already guess, I was not interested in the solution of these equations to help the world create better antennas for modern smartphones; I simply enjoy modeling equations, and feel extremely satisfied when filling over 200GB or RAM memory and waiting for one whole day of computations to solve a single equation!
For many years, I considered academia to be superior to industry. There were two main reasons for this: it’s where I have met some of the most brilliant minds, and because I believed that academia could chase a more pure form of science free from the laws of profit. But at some point, the first cracks in my beliefs started to show and I realized that academia is not always the idyllic scenario I forged inside my mind. I decided to give industry a try and ended up joining the largest manufacturer of photolithography machines. There, I realized industry also has two of the features I look for: talented people, and interesting and challenging problems to solve. But there was still one missing piece in my personal puzzle…
I ran into Q Bio by accident. Towards the end of 2018, I was trying to contact Athanasios Polimeridis for very unrelated reasons. We had known one another from the field of computational electromagnetics and, years ago, had chatted at conferences around the world. I had some questions for him about one of his contributions to the field. That’s when I learned about his position at Q Bio. Of course, curiosity made me take a look at what Q Bio was all about and I had my first encounter with the term “precision medicine.” I have always considered medicine more a sorcery than a science, but I had my epiphany: healthcare can be addressed in a completely different way, a way that in my eyes makes so much more sense! And the idea is so intuitive and effective that I felt stupid to have never thought about it myself. By tracking snapshots of the health of each individual over time, it is possible to detect and identify changes in our body before symptoms start to appear. It is the first time I’ve been extremely happy to have been wrong all my life!
What’s more, building these comprehensive snapshots involves addressing some of the challenges I love! The Q exam can include, among other things, a full-body MRI scan. Some of the problems we have to solve to enable a comprehensive and quantitative approach to MRI require a lot of physics, mathematics, and high performance computing. My puzzle is finally complete: talented people, interesting mathematical problems to solve, and the noble goal of doing our best to improve healthcare. Q Bio is not just an MRI company, but if you join the modeling team you are definitely going to be exposed to a fair amount of MRI physics. And if you are like me, you are going to have a lot of fun while doing it!
How to address bias in medicine against women. Or why we should take inspiration from Taylor Swift, Lizzo, and Serena Williams when it comes to health equity.
This post was originally published February 4, 2020 on Thrive Global. We are sharing it on our own blog as conversations about health equity are rising. Women and low-income people of color have been disproportionately impacted by this pandemic. Original post below. We can and must do better.
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With all that is going on in the world, one area that we should be most optimistic about is how the medical community is taking bias in medicine head on. Especially where it has been skewed against women and even more so against minority women. There have already been many alarms sounded and research papers written on this subject in the past decade…and even a John Oliver take on this issue. But in this new decade, it will become headline news and ever more public alongside conversations driving the #MeToo movement, gender inequality in business and media, gun violence, and voting rights. Women have led the conversations in those arenas; they will be the leaders in health equity as well.
“Hey, just so you know, we’re more than incubators.” — Taylor Swift
Women, especially Millennials as they advance in their careers and lives, have already driven investments and open conversations around fertility health. That’s been great, even as more research needs to go into understanding pregnancy and how that affects overall health. Women, pregnant women, and minority pregnant women, are under-researched, under-represented and usually purposely excluded in studies.
An even greater gap is that there is such a focus on reproduction. Women’s health should not be synonymous with reproductive health. Many of us only see our Ob-Gyn as a stand-in for primary care. Our annual health exams, when we hit puberty, is all about our sexual health. When we’re young, and if we’re lucky, there’s some sex ed thrown in hopefully taught by trained youth advocates. As we age, the focus is on mammograms and pap smears.
Yet, this excludes the fact that annual exams are not comprehensive for women. The leading cause of death for women is heart disease. Respiratory and pulmonary disease have been on the increase worldwide for women. And up to 78% of those with autoimmune disease are women. Our health should be understood in the whole. We are more than just our reproductive system and we deserve a full system biology view for greater preventive health.
“I just took a DNA test, turns out I’m 100% that bitch.” – Lizzo
From a health tech perspective, women should be empowered with their own medical data. Taking a DNA test, we should come out empowered by the information. And, importantly we, women and men alike, should own our own data. Just as there is a lot of disinformation now online and out in the world, clear data should shine the light on what is truth. In a world where women’s pain and instincts can be overruled and unheard, we should be able to bring data to the table. We should be our own truth-sayers and empowered to point to clear information so that we can not be ignored.
There have been too many of us who have family and friends who have suffered because they were not heard. I have had a close family friend finally get a correct diagnosis too late after multiple doctors; the disease was already advanced by then with limited treatment options available. I have seen loved ones turn to pseudoscience and understand the appeal when you feel that other tools available in the medical system are blunt. When you are not listened to, it’s easy to turn to those who seem to at least hear you and believe you, even if there are no clinically proven solutions. There should be more science and less “art” of medicine. There should be bias training and more personally empowering and accessible data for individuals.
Looking ahead at these next 10 years, I’m optimistic about the changes and inspired by the many advocates raising their voices together. Here’s what we can all do:
Commit to having a better understanding of your own personal health baseline and your family history; the more you know about your genetics, your physiology, your metabolism, the better.
Speak up and ask your doctors/specialists for resources you are curious about. No, we may not all be medical professionals, but we know our bodies and we should know our options. Options matter.
Talk about it with friends and family. Having a sounding board about our well-being is just as important as being heard by medical professionals. We can gather more information through our own trusted network to help us make better decisions.
Here’s to less bias in medicine. Let’s get more accessible and better data out. And believe in Science. Believe in Women.
As shared with all our members this week, we have opened our Redwood City Q Center as of this Monday, May 4. We closed over the last several weeks out of an abundance of caution and to enhance our protocol to meet the challenges of today’s new normal. As many other businesses look ahead to what it means to re-open, we wanted to share the additional steps we’ve taken to keep members and our staff and community safe, as well as new protocols we have added to benefit your health monitoring.
As those of you who have already been to our Q Center know, we schedule each visit for you personally and there is no wait time in a reception area where you could encounter other members and risk community exposure. Our check-in has always been contactless with your individual QR code sent in advance with your registration confirmation. These protect not just your privacy, but also any additional contact-driven exposure.
We have always exercised strict sanitation and cleaning standards and have increased our protocol to the strictest Universal Precaution Infection Control protocol recommended by OSHA and WHO. It includes but is not limited to:
All equipment that touches each member is either individually packaged or sterilized after each visit
Protective gloves, masks, and eyewear, will now be worn by our clinical staff at all times
Each member is provided with a disposable mask, change of clothes, slippers/socks, and hand disinfectant upon arrival. Additionally, there is 70% alcohol hand sanitizer, liquid soap, and paper towels throughout our center for member use
A new Level III face mask is worn by each clinical staff for each member visit
All areas of our Q Center are wiped down in between each member visit using hospital-grade disinfectant with demonstrated effectiveness against emerging viral pathogens, similar to SARS-CoV-2, on surfaces. Additionally, UV-C light is used to sterilize equipment and all private member exam rooms
We have installed hospital-grade air filtration systems in all rooms of our Q Center to prevent any airborne viral loads
In addition to these precautions, which will be in place whether there is a known outbreak or not, we will also be sending out a questionnaire in advance of member visits that cover specific questions about your travel, exposure to travel, and your health immediately before your visit. Temperatures will be checked at the door and we will be rescheduling any members should there be potential risks that are flagged. We thank our members for working together with us to keep everyone safe.
Perhaps more importantly, we have added serological antibody tests for COVID-19 to the Q Protocol. This has been determined to be clinically-relevant for tracking COVID-19 exposure over time. You will be able to test if you have had COVID-19 exposure and have potential immunity. We will continue to update this test as the research is ongoing to provide the most reliable and reproducible test available as this field continues to quickly update. We are offering this additional test to all members with upcoming Q Exams during your visit and also to all existing members as a follow-up service. If interested, you can email firstname.lastname@example.org for more information and to schedule an appointment. We plan on offering this test to all our members on an ongoing basis to help track any exposures over time.
Additionally, we are testing and researching acute COVID-19 tests to provide to members who may have current exposure. We will keep you updated with the newest developments.
We hope you and your loved ones are safe and hope to welcome you very soon to our Q Center. Be Well!
In the recent episode of Hyper Wellbeing podcast, our Founder/CEO Jeff Kaditz begins with coronavirus chat. He goes on to explain that most medical knowledge today is probably incorrect or heavily biased. That there’s almost nothing a doctor does that couldn’t have been done 200 years ago in terms of the information.
He presents his vision to run ‘search engines for the body’ and turn healthcare into hard science.
I was looking for challenges as deep and interesting as the ones I had at Tesla, and finally found them here at Q Bio.
My journey in the technical field starts across the Atlantic, in Italy. Early in my career I designed boards and developed tightly integrated software, and moved on to larger and more complex systems in Silicon Valley, culminating in work on the largest battery in the world, fleets of hundreds of thousands of IoT devices and special projects on the Model 3 and Superchargers.
All these experiences gave me an appreciation for the magic required to make such products a reality, so I had quite a list of ideal requirements when I started looking for my next challenge:
An extremely collaborative team
With an inspiring mission
Focused on deep innovation and tackling big technical challenges
Not afraid to challenge the status quo
And at the same time, very pragmatic (for example, adopting technologies that help move the mission forward, not just for the sake of deploying new, cool technology).
Team and mission
When I met the team at Q Bio, I was impressed right away: a super-diverse group of extremely talented scientists and engineers, and a company that met all my ideal requirements (to top it off, I’ve since discovered that the team is extremely fun to be around).
In addition, the company’s mission became crystal clear to me after talking with Jeff Kaditz, Q Bio’s CEO and Founder, and that sealed the deal.
My work here
Here at Q Bio, as part of the Radiomics team, I design and develop systems that span the entire pipeline, from a Magnetic Resonance scanner all the way to Q Bio’s cloud services that support our members. I write software, help select hardware and think hard about streamlining our systems.
It’s also a fantastic opportunity to be involved with MR scanners, an amazing feat of technological innovation (how often do you get the chance of operating a $1M machine?) and a greenfield opportunity to define the systems that will enable more and more advanced experiences for members.
I know I do my best work when the company’s mission is fully aligned with my personal values, and when my young son asks me “Dad, what are you working on?“, I’m happy to have an answer I’m proud of. At Tesla, we were accelerating the use of renewable energies to solve a big challenge of this century. At Q Bio it is about ushering in a new era of better health through innovative ways of measuring the most important changes in the body, and this, for personal reasons, resonates even more for me.
We have a phenomenal team and are tackling an incredibly important challenge.
If this resonates with you, we are hiring. Check us out and join us in this very special journey.
The phrase, “the art of medicine,” has bothered me for many years. With technology already at the forefront of medical discovery, and improving everything else in our daily lives, why do people still die from treatable conditions? Why haven’t we applied the same scientific principles that have led us to understand the evolution of the cosmos, weather patterns, particle physics, or planetary motion to the human body?
When Life Gives You Lemons…
I recall standing in my office in San Francisco years ago, waiting for an update to a mobile analytics platform my team had built, the second largest of its kind in the world at the time, with hundreds of millions of active users all on their cell phones, all over the world. It became clear to me that for the first time in human history we could begin to measure and quantify human behavior. At that moment I felt I was witnessing the transformation of sociology from an art, left to academics, to an information science.
I’d finished early on a dual degree program in Computer Science and Physics, mostly because I had been told it was impossible (the single best way to get my attention), and had consequently spent my post-college years starting tech companies that solved problems ranging from network security to consumer lending, punctuated by chunks of time off to recharge by skiing in remote places. The master plan, despite my father’s insistence on an MBA, was to move to Wyoming and spend the rest of my life chasing winter, but something I could never have imagined derailed those plans.
In June 2008, I was clipped by a car while training for an Ironman. The impact dislocated and shattered my left hip and cracked my pelvis. I also tore muscles off of my right elbow, and a quadricep off my right knee. I had massive internal bleeding, could only move my left arm, and spent much of the year bedridden. At one point I was told I had advanced avascular necrosis in my hip and if I didn’t get a replacement I would lose my leg. I was able to avoid a hip replacement, but still required major surgery after dealing with months of conflicting diagnoses, and struggled to get hospitals to share information about my body, which delayed my recovery.
On top of this health crisis, the driver who hit me was uninsured, and my insurance company refused to pay for the critical care I needed, pending an investigation. The financial crisis of 2008 compounded these problems, and I was forced to sell my house and everything else I owned in order to cover the hospital bills. In three short months, once in great health and financially secure, I found myself unsure of everything: I didn’t know if I’d be able to walk again, let alone live the life I imagined, and I was broke.
What I did have, however, was a front row seat to the complexities of both the finance and healthcare systems, and the better part of a year in a hospital bed to consider how some foundational concepts in both needed to change. My reimagining of the lending industry ultimately led to the creation of Affirm but rethinking healthcare presented a more interesting, complex problem. As an athlete, dedicated to my own health, I found it bizarre that not one doctor could determine what had changed in my body due to the accident, and efficiently assess my condition and treatment plan. But as a scientist, I wondered why there wasn’t a tool with which to comprehensively know the state of our health on a regular basis — not just when we’re sick or confined to a hospital bed, but all the time, and perhaps even before small problems become big ones.
Building the Q Bio Platform
In 2015, Q Bio was born, and we set about to first consider these key principles:
1. Why We Measure, What We Measure, and How
Every person reading this will get sick or injured; it’s inevitable. Our concern should be making sure when this time comes that our doctors have the best tools/information available to determine the cause of the issue, when time is of the essence. The most valuable thing to know at this time is simply what has most recently and significantly changed. This isn’t screening, this is preparing for something inescapable, and we call it “health monitoring”. With this in mind, we designed first platform able to comprehensively measure and identify clinical changes in human biology, associated with common causes of death. In less time than it takes for an average dental visit, Q Bio measures thousands of genetic, biochemical, and anatomical biomarkers. Our platform then continuously aggregates and analyzes a person’s medical history, looking for relationships between past or recent health events and changes in a person’s body that may increase risk. Ensuring this process is non-invasive and fast is critical so that it can be done regularly and reproducibly. We believe this is the physical exam of the future.
2. Clinical Value and Actionability
A research team including Dr. Michael Snyder, one of Q Bio’s founders, studied a group of more than 100 patients for up to eight years, measuring data on them every quarter. During the study, the researchers discovered more than 67 potentially serious health issues, which would not have been discovered as early, if at all, without this level of data analysis over time.
It’s simple but true: every human body is different, and even genetic twins make decisions over the course of their lives that make their risk profiles diverge. The best way to know if there is an issue emerging in your body is to compare you to you. Most diseases are accelerating processes, so assessing health risk on an individual level based on what is changing and how fast will yield insights about the progression of disease far better than comparing single measurements about you today with outdated, unrepresentative population references.
At Q Bio, we believe firmly that this tool can dramatically affect the outcomes of your healthcare decisions for the rest of your life.
In order to make sure there is clinical value in the Q Exam, we consider every biomarker we measure with two specific characteristics in mind:
How well it can be reproducibly measured
Existing clinical evidence relating a biomarker to specific health issues
While we are excited about all the research going into the discovery of new biomarkers and tools to measure them, many of them do not sufficiently satisfy these criteria, which we think are critical in order to make information actionable for clinicians and increase confidence in clinical decision making. So we have focused on making better use of existing biomarkers to make sure we are providing immediate actionable value to our users and partners, while continuously evaluating and integrating the latest biomarkers into the Q Exam as they are ready for clinical use.
Actionability is an important characteristic of clinical information, but there is a difference between actionability and clinical intervention. Having actionable information also means knowing when the best course of action is to do nothing. Too often in our health care system do we intervene with drugs or procedures due to a lack of good information and then do limited follow-up to gauge if that intervention not only had its intended effects, but to make sure it didn’t have any unintended side-effects.
3. Empowering Doctors with More Information Requires New Tools
An important part of our mission is to build technology that makes doctors more effective, so that they can spend more time with their patients who need it most.
Today, a single physician can see about 2,000 patients a year and has an average of 15 minutes to spend with each, a significant amount of which is spent logging opinions into an EHR. Highly skilled labor is an increasingly scarce in today’s healthcare system, and this is an ineffective use of their time. If we want preventive healthcare to be available to a growing population, we either need to make more doctors, faster, or doctors need to spend less time with each patient on average. In other words, we need to give doctors the tools so that they can focus more time on people who need it most, and less with those who don’t.
To this end, we designed the first platform able to quickly sift through vast amounts of information and surface the most relevant clinical chemical and anatomical changes in a person’s body broken down by the major subsystems, weighted by their genetic, medical history and lifestyle risks. This removes the obligation to pore through EHRs, which are designed for billing and administration, not to help a doctor understand the dynamic factors impacting someone’s health. Allowing doctors to quickly find emerging issues and identify individuals who have no major immediate risks saves time.
4. Empowering Individuals
The rise of wearables, smart scales, etc. is driven by the underlying desire of people to have better access to and control over information about their bodies. Ironically, the vast majority of this information isn’t clinical quality and cannot be easily used by healthcare professionals in their decision making. Q Bio is the obvious next step in empowering people, not just with more information, but better information, with actual clinical utility so that it can be incorporated into their care. For the first time ever, Q Bio gives people complete control over this information and with whom they share it, making slow, painful processes like second opinions, or re-testing things of the past.
That’s what we are all about at Q Bio. There is growing evidence that a data-centric approach to healthcare will lead to better outcomes. We have the technology to comprehensively measure the human body, tracking clinically relevant and measurable biomarkers for individuals. We have built the software that, with today’s computing power, can analyze the data, and see trends over time, and give that information to individuals and their physicians in an actionable way, as defined by a higher standard.
The Paradigm Shift
We can change medicine from art to science. We can empower doctors to take better care of more people and give patients the efficiency and privacy they need. We can build a healthcare system that does far more than screen for disease and react to problems once they’re existential. It’s a fundamental transformation of medicine that we can and must make.
That’s why we built the foundation for the Science of Medicine; the first platform ever designed to comprehensively and efficiently monitor changes in human health.
For most PhD students, the question of what to do after you graduate can seem to pop up around every corner, often beginning long before graduation is in sight. The underlying questions, though, are about what you value and how you want to channel the skills you’re building. I’m pleased to share the next step in my journey after PhD and postdoc: I’ve joined Q Bio as an MR Engineer.
Of course, searching for the next step isn’t unique to graduate school. I first gave it real thought when I was graduating from high school. I liked engineering, and I liked the idea of using it to improve health in some way, so I joined a biomedical engineering program. Towards the end of my undergraduate study at Johns Hopkins University, I worked on identifying areas of brain disease in patients based on whether tissue appeared bright or dark in different types of MRI scans. After graduating, I began using MRI to better understand mental health, neurodevelopmental differences, and information processing in the brain.
While working on my PhD at Vanderbilt University, I learned about using focused ultrasound to treat cancer and neurological conditions non-invasively. Imaging during treatment is essential for success, and I developed ways to use MRI to improve treatment monitoring. As a postdoctoral fellow at Stanford University, I validated an MRI technique to image where the focused ultrasound is located within the brain, in order to verify treatment plans prior to therapy.
I’ve arrived at each of these stages by following what is meaningful to me. I want to make a difference in the lives of patients and in healthcare. At Q Bio, we’re building MRI and bioinformatics tools to characterize complex physiological conditions. By integrating these measures, we build snapshots of peoples’ health over time and identify deviations from a healthy state. Our goal is to provide actionable results through an accessible, safe, and comprehensive health exam that empowers people to know more about their own health.
I’m excited to share that I’ve joined Q Bio as VP of Radiomics. My journey here has been a long time coming. Even before entering college, I had decided that I wanted to focus on radiomics. I thrived on solving problems since I was a boy, but I was really bad at playing computer games. I could never win! And often I ended up just hacking my computer. My mother always thought that my computer was broken because I was modifying it so much. When I first heard about the ability to view the inside of humans with computed tomography, I built a viewer for MRI images on my computer… and I was hooked.
And so I oriented my education in that direction. After I landed my first job in a fMRI lab during my first year of college, MRI machines became literally everything to me. Some 15 years later, after completing my PhD at MIT, I became the head physicist at one of the best MRI research laboratories in the world, the Athinoula A. Martinos Center for Biomedical Imaging at the Massachusetts General Hospital. I got to solve problems that are meaningful to me and to take care of a family of 8 human MRI machines. This involves, besides QA, technical diagnosis of problems and pulse sequence developments. I also supported and collaborated with several hundred users of the center. My computer science side has not retired, so sometimes I find myself debugging Linux kernels, chasing down BIOS bugs, programming FPGAs, …on top of soldering custom equipment I developed.
My work interests focused on two areas. One is the optimization of clinical MRI procedures and to translate new MRI techniques into the clinic faster, which will ultimately allow for better diagnostic quality as well as increased patient comfort. MRI has been incredibly important for modern medicine and it’s a prominent tool in diagnostic medicine and biomedical research. But it’s also been expensive, time-consuming, with poor reproducibility and as such, only used in acute circumstances.
As a tool, MRI captures chemical and physical data, in addition to generating detailed spatial images. And importantly, it does not expose the human body to any radiation as with other more invasive imaging technologies. There is an opportunity to think about focusing this technology on health and not just on sickness. What is possible when we use it for preventive care? What will a whole body scanner of the future be like? In many aspects, today’s MRI machines are still confined by the basic principles set over 30 years ago. With modern computers and appropriate generalized algorithms and low-cost multi-modality sensors, many of these principles are no longer valid and the hardware can now be rethought. It was time for me to get a bigger garage.
When I met Jeff and the Q Bio team, I saw that they shared the same passion and conviction in making whole-body scanning part of the standard physical. They are also already up and running with a rapidly growing service that has helped individuals and their clinicians gain valuable insights about their health and their bodies. The team has already caught early diseases that have significantly changed the health outcomes of individuals — actually saving lives. Real-world impact and the potential to build and learn together with an interdisciplinary team. I decided this was where I wanted to build that garage.
At Q Bio, I’ll be leading a team to help the larger interdisciplinary Q Bio team to make cutting-edge morphological measurements over time a reality. Together with clinicians, software engineers, and bioinformatics analysts, we work on MRI physics, hardware debugging, sequence programming, advanced reconstruction, auto-segmentation, C++, Linux… and in between, enjoy some hands-on building and playing with magnets and novel sensors.
Our mission at Q Bio is to make it easy for individuals and clinicians to measure the most important changes in the body to help identify disease at its earliest and most treatable stages. If interested in this mission, please reach out. I’m hiring!
I’ve jumped in to the deep end again. And it’s one of my favorite times of building and scaling a business. The early team of less than 20 have worked with the founder to prove early concept and solved some of the hardest technical product problems. Early adopters are returning as users and word of mouth is driving real demand. The early glimmer of an idea has been substantiated and now lives and exists at the core of the company. Yet there are still many unknowns and so much to be figured out. But there’s a team here willing to work together to solve hard problems and actively learn together. This stage of a company — early and at an inflection point in building visibility, brand, usage, membership, and becoming a full-fledged business with lasting social impact is incredibly exciting. One of my favorite times to jump into start-up trenches.
So here I am at Q Bio. I was incredibly inspired by my early conversations with Jeff, Garry, Thanos, and their early investors and board. Their vision and mission: treatable diseases no longer take lives, and every generation is healthier than the last. That’s a big and dynamic peak I’m motivated to scale. I’m honored to be able to join them on this mission.
Before joining, I had the rare privilege of being able to take 7 months off. In that time, reading, talking with friends and family, traveling, playing with new ideas, I have always come back to what makes it worthwhile to work. For those of us lucky enough, our time is the most precious resource we have. And this is amplified for me with 3 children at home who can always use more of my time. The mommy guilt is real. Yet I like to work hard on hard work and so where and how has to be meaningful if I’m away from home. Improvements in education, the environment, and health care have always been the 3 areas I felt would make the most difference in my children’s life and future.
The team at Q Bio is solving for truly actionable and hard science. They take their mission seriously. And what’s really inspired me to make the jump to Q is a set of core beliefs and how they are approaching building a solution that aligns with where I believe health care is going / needs to go. The team here believes that…
.…Prevention is better than the best treatment
This seems perhaps obvious. Those of us who try to exercise, eat healthy have absorbed this belief from a young age. I do this for my kids as it’s required for vaccinations, well-baby / well-child check-ups, and there’s a given schedule. My oldest, however, is already aging out of this schedule. It’s crazy that there’s a gap starting with older teens and young adults where we no longer have annual check-ups and only reactively go see doctors. For women it’s a bit better with ob-gyn check-ups, but the last time my husband and I had a comprehensive exam was in Taiwan over 4 years ago where there are more affordable and welcoming options. As Jeff Kaditz, Q Bio’s CEO/founder likes to point out, seeing our dentists regularly is the only model of regular, ongoing check-ups we do for our health. It’s not just anecdotally important, but lifestyle and prevention as medicine represents at least a 40% opportunity to improve population health.
…System biology that brings affordable, non-invasive radiomics side-by-side with clinical biometrics will revolutionize our understanding of the body.
To tackle this, Q Bio is looking beyond targeted treatment populations. Instead, the team takes a system biology approach and focuses on known markers and actionable insights for a broad and overall healthy population not worried about immediate disease treatment. The focus is on preventive medicine. As it turns out, all of us have something that may be “off” at any given time that does not require intervention. Or many of us manage and have under control some health concern, but don’t really have a full known treatment available. I have allergies that come and go without any clear understanding of what triggers them. There’s much that medicine still does not understand in terms of what a spectrum of health looks like. The approach of studying just single parts — whether genomics, microbiology, or focus on specific tissues or metabolic systems — feels like the parable of the blind men and the elephant. The conversations around inflammation and what it really reflects; or the recent unfortunate failures in Alzheimer medication trials because, as it turns out, there are larger systemic dependencies that a single path of treatment can not solve; these are all examples of where having interdisciplinary and holistic data would help us better understand our bodies. At Q, the specific focus has been to bring non-invasive, repeatable, and comprehensive radiomics together with known biometrics (i.e. data from genetics, blood, urine…) to provide a full picture of individual health.
…Actionable, individual changes in your own health over time is better than single point in comparison to population average for health baseline
And they are looking at this over time. The goal is to provide a big data, longitudinal view of health. The underlying thesis being individual risk factors are better indicator of health than comparisons to larger population averages. For those who are quantified health geeks, this goes beyond sleep trackers, FitBits, Apple Health, to look at known and actionable clinical markers. Note that this is not about research level omics, but replicable and known markers. And the goal is to catch any potential for disease early so that the focus can be on prevention and to not drive individuals to overtreatment, but instead early engagement before reactive treatment required. Interestingly, Q Bio to date has found that in about 21% of visits, clinically significant high risk factors affecting mortality and still at an early stage were found that informed clinical decision for early intervention or additional diagnostic evaluation. This represents significant cost savings in healthcare and, more importantly, better individual health outcomes. All a result of tracking early and personal baselines for individuals.
…Individuals should have access to and control over information about their health and bodies.
Finally, the team is committed to putting members first and engage with an individual’s chosen community of care providers, no matter who or where. This means focusing on privacy from day one and having high controls in place on how we collect data. I have not met many start-ups that have IRBs in place from the get go. And from a data ownership standpoint, so much of healthcare can be frustratingly truncated or locked within a system. To get second opinions, to move, to change providers often means a loss of your health history. And many companies in this space keep information they gather over individual health as proprietary. I really like the trust that Q is building with members by committing to provide full access, portability, and control over their health information. This is about truly empowering members.
At Q, we are building the physical of the future.
For any of you who’d like to jump in as well, please reach out @clarissa_shen! Come take control of your health and join us on this mission to make every generation healthier than the last.
We are entering a new era of data-driven health monitoring. In addition to conventional approaches, we can now determine genome sequences, collect data about thousands of molecules (RNA, protein, metabolites, lipids), perform advanced imaging, and continuously monitor physiology.
Importantly, we can follow people over time, during periods of disease and health. In today’s Nature Medicine article, my scientific colleagues and I describe the results of a research project called Integrative Personal Omics Profiling (iPOP) that illustrates the value of using advanced technologies to carefully follow 109 people for about three years (many for four or more years) and how this can be applied to manage health. This study uncovered 49 clinically significant health findings — plus 17 more if hypertension is included. Some of these findings were very consequential — early detection of lymphoma, two precancerous conditions, and two serious heart conditions. There were a variety of disease risks identified (e.g. for cancer, cardiovascular disease) and early signs of disease (e.g. diabetes) that were actionable.
The ability to focus on early intervention and prevention represents significant cost savings in healthcare and, more importantly, better individual health outcomes.
One of the core beliefs of this approach is that tracking individual changes over time is better than examining population averages alone in identifying clinically actionable, early health interventions. This study confirms that belief. Population health data inherently looks at averages and may miss early signs of disease progression in an otherwise asymptomatic individual.
Existing medical knowledge is biased and there is a need to de-conflate the measurement of our biology from the analysis of our health. Tracking a well-defined set of biomarkers longitudinally offers the clinical advantage of detection at the earliest stages of disease where an intervention may be more likely to succeed in reducing long term morbidity and mortality.
Developing the Physical of the Future
While presenting the iPOP project over the years and all over the world, many people have asked how they can get access to these technologies to follow their own health. Consequently, Jeff Kaditz, Garry Choy, and I have spun off a derivative of iPOP called a Quantitative exam (or “Q” for short) which is offered by Q under an institutional review board (IRB) approved protocol. The Q protocol brings together many of the health-related features of iPOP and adds whole body MRI (magnetic resonance imaging). We began piloting the Q protocol in 2017 and have since expanded to include select partners, and interest is high.
The Q protocol has already shown promise that goes beyond iPOP by generating additional data around known and actionable set of biomarkers that includes non-invasive, whole-body, comprehensive imaging data. This has allowed the team to not just track personalized, longitudinal changes…
…but to track these changes at depth across specific biomarkers.
By utilizing a multiomics approach similar to iPOP but further incorporating biometrics and non-invasive radiomics, to date Q has found information for at least one previously unknown health-related condition in 97% of member visits; this information is valuable for ongoing preventative monitoring. It is also further evidence that comparing against population averages does not really reveal much about the line between health and sickness. Given the healthy population served, the large majority of members did not have follow-up care required. However, in 21% of those first visits, the Q protocol uncovered clinically significant findings that were both high risk for affecting mortality and at an early stage informing clinical decision for early intervention or additional diagnostic evaluation. The ability to focus on early intervention and prevention represents significant cost savings in healthcare and, more importantly, better individual health outcomes. To quote one Q member: “Nobody has ever told me so much about me.”
We are optimistic that longitudinal deep profiling, accompanied by powerful data integration and analysis, will ultimately help improve healthcare. Individuals and their healthcare providers will obtain a clearer picture of disease risk and evolving health status. As one Q referring physician and member recently shared, “Q does a great job leveraging advanced biomedical science and technology to assist primary care providers to provide more informed, precise, proactive care plans to their individual patients. Q brings the ‘possible’ in the future of medicine to the actual care delivery in the clinic, now.”
Leaders in executive roles, for which long hours, extreme stress, weight gain, and sleepless nights are generally expected, are prioritizing their personal health to increase their life expectancy spent with loved ones and improve their long term quality of life. Additionally, it is now seen as mission-critical for corporations to ensure that the health of their leadership teams, a key asset, is optimized. As a result, the demand for executive health programs continues. Conventional health clinics, as well as new precision health companies, offer executive health programs aimed at executives who require a comprehensive and deep health assessment while catering to their hectic work and travel schedules. The exam comprehensiveness, cost, and accessibility vary across a wide range of executive health programs — that’s why it’s critical to understand and analyze your options.
Five considerations when evaluating executive health programs:
A comprehensive approach. Many health institutions offer executives basic physicals and run-of-the-mill stress management classes, but there are executive health programs that go above and beyond traditional offerings. Some executive physical exams provide a full body MRI, genome sequencing, family history analysis, disease screenings, and much more. Today’s technology allows for non-invasive biomarker measurements that track changes in the body associated with everything from detecting risk for cancer to neurodegenerative diseases.
An exam duration that makes sense for packed schedules. Anyone looking to take advantage of a traditional executive health program at an institution should expect to block up to two full work days for testing (which in itself can create even more stress). However, some innovative executive health programs have been able to reduce the full comprehensive exam time to 90 minutes or less.
Competitive pricing. Executives seeking comprehensive testing and care at a conventional institution will have to do a lot of digging to get to the bottom of what to expect cost-wise. The price of the exam is just one component of cost. Factoring in the cost of travel, multi-night stays at a hotel, etc. should be considered in assessing the total cost. While a lower total cost will have immediate saving benefits, it can also justify getting executive physicals more frequently and build a more longitudinal understanding of your health risks.
An accessible location. Consider the travel time to get to your executive health program. Some executive health programs are scattered from Arizona to Florida, taking significant time away from work and family. Finding an executive health program locally offers convenience and accessibility that executives should factor when weighing executive health program options.
Top-notch credibility that speaks for itself. It’s critical to stick with an executive health program that has a strong reputation for clinical excellence. It is your health after all. Look to the leadership team for guidance: doctors from elite academic and medical institutions can indicate a solid choice. These doctors typically apply the latest research and findings in their approach while also avoiding trendy but less clinically actionable testing. Many executive health program leadership teams have developed next generation research and protocols in fields like genetics, medicine, and more.
To learn more about Q Bio’s industry-leading, comprehensive and accessible executive physical program, visit https://q.bio today.