a16z Podcast - The Future of Primary Care

Episode Date: June 11, 2020

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.In this episode of the a16z Podcast, we talk about what the primary care of the future should actually look like; what kind of data about patients we should be collecting, from where, and to tell us what; how you ask the right questions of that data, to use the resources of our  healthcare system most efficiently and for the best care; and what the PCP of the future might look like. Joining us for the conversation are General Partner Julie Yoo, physician entrepreneur 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. 

Hi and welcome to the A16Z podcast. I'm Hannah. Primary care was meant to be the front door to the health care 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 health care system most efficiently. In this episode of the A16C 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 U, physician entrepreneur, Dr. Iverhorn, a primary care pediatrician for more than 20 years, and Jeff Kedz, CEO and founder of QBio, 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 health care 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 are we learning about the cracks in primary care from this particular moment? 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 and the space where there were limited resources and overwhelmed that area. And there weren't the opportunities taken to use other mechanisms such as telemedicine or telephones to communicate with people and to do that triaging that needed to be happened rather than people being exposed.

even in the doctor's office. Yeah, it is the what we call low acuity sort of entry point for care, whether it's a sniffly 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 sort of expensive and more complex type of care setting and, you know, essentially get their needs taking 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 is primary care 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 with sometimes months, right? There's just a very fundamental economic fact, which is the most scarce resource we have in health care 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 could do in healthcare. This whole flattening the curve, just in general, primary care should be about flattening the curve. And flattening 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 kind of on a continuous 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. 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. But that time is of use in used appropriately and well. So at the moment, this sort of triaging is done in like the most inefficient, klutziest way where people are literally left in a giant vacuum of trying to get on, you know, in a telephone queue and describe some vague symptoms that one person may describe in like a completely different way. You're talking about a different kind of both

support and information gathering for that type of triaging. So let's talk about what that could look like. Traditionally in medicine, you measure something if you want to diagnose something. And I think 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 diagnosis of 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, right? And then there's high fidelity, low frequency data. And there's lots of information

between. When actually an 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 gathered very effectively so that 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. Can you give an example of what that looks like? Well, I think it's different levers of triage. I think you, in theory, could be monitoring somebody at home, and based on changes in

risk, say, we think you need to get a lipid panel done, right? And then based on the result of that livid 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 it, like an 18-year-old healthy person with no health risks, takes time from a person who is having like severe chest pain. you know, and has a lot of indicators that says 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. What'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 subspecialist for that matter. Yeah, and 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, you know, kind of movement of health care into the home and the fact, like, 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, you know, had you done that test up front 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.

You know, 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. What is it that's so hard about just flipping that one simple thing? Like, 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? There's a general problem that we're talking about, which is overload, right? Like, that's why flipping the switch is hard is because there's a whole class, there's a new class of clinical decision support tools that needs to be there.

Otherwise, you're actually creating more work for a doctor. Like, if you measure a thousand things about every person and a doctor's expect to look through those things, that's not reasonable. So you need to have intelligent tools that can actually highlight the key things. Because it like flips the whole paradigm on its head, right? Because like 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? But 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 is 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 that, 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 in how we interpret that data, right? And what comes up in that algorithm or what comes forward in 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 and 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 and care. 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 weight the significance of the measurements based on genetics, lifestyle, medical history. I think the right way to kind of 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 this in context and 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. One of the paths to overcoming these challenges that you're describing is actually to go, you know, 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, right?

You know, Jeff, you said earlier, the low frequency, high fidelity data points. Like that's pretty much solely what we depend on today in traditional medicine and traditional primary care, whereas like the vast majority of insights that probably determine, you both your current state as well as what your progress is going to look like over the course of time comes from everything else, like all the social determinants and behavioral and demographic-related information that Ivers describing. And part of the challenge of why we have so much bias and why it's 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. I mean, 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. I mean, if you think about EMRs, they're really designed to administer and fill, right? Most of the information we have in EHRs or are biased towards sick people, they're biased towards people who have access to care. And when we talk about like a health care system that gets better, unless we can decouple measuring the human body from kind of care

decisions, which are opinions at the end of the day, their predictions, like saying this person to do this, we will never actually close that feedback loop because we can't look back retrospectively and say, okay, could we have made, knowing what we know now, would we have 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 in thinking about almost a person that goes out and interacts with their environment as a sensor, I actually see the future of health care being able to prevent things like Flint, Michigan. If you were actually monitoring the population and the clinicians

had access to the information, you'd see a change in population health as soon as those water pipes were switched, not two years later when it was damaging kids' neurological systems. Understanding all of those social determinants of health, one of the things that we've learned as part of this process is 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 a 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. And all of those things impact on the data that needs to come forward. When we talk about social

terms 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 and a social network that impacts on 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 that provider being able to give more effective and more useful guidance to that person when they come in when they've been triaged accordingly. So other levers you can pull besides a prescription, besides a diagnostic test,

besides an office visit, but communities and support. Exactly. And some of those things can be done via telemedicine. We often think about it in this one-on-one video perspective, but there's a one, there's a lot that you see in a telemedicine visit that's around a person that gives you context. The other is the simple use of a telephone conversation and using that as a tool for checking in and that being an important factor in making sure that we're creating more longitudinal data. The value of longitudinal data is so important that we don't take into consideration because we piecemeal it together, as you said, in those low-frequency, high-fidelity EMR-type visits, but we have that sort of those more frequent steps that we get that actually

broaden our understanding of a patient in ways that we never could do before. I actually think the key to personalize 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 risks, what is the subset that actually needs to be monitored about this person and the frequency? And all this telemetry is just connected. That kind of first order triage or the collection of data should almost happen passively without a doctor having to worry about that the right things are getting measured. So when the time comes and a person is, let's say they have 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 a doctor, the doctor starts with almost nothing, or in the ER, right? And it becomes an information

gathering journey before any decision can be made. I hear such a sort of tsunami of like new types of data available that are, that can be incredibly valuable, aren't being used the way they should. And also, like major shifts in the entire kind of 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? 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, right? And so I think like 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.

Part of why we're in this challenging spot is the fact that we were reliant on a system that only paid for individual tasks and therefore, you know, 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, you know, 10 years ago, but are now absolutely the direction

that primary care in particular is headed. And we see that in light of programs like the primary care direct contracting program with CMS, you know, more and more ACOs, you know, getting traction with even commercial payers, et cetera. You got to realize that really a little over one and nine people actually have health literacy enough to understand how to manage their health care and manage the health care system when you start talking about health literacy. So the ability to communicate and translate that information into a way that people can effectively provide and support themselves in their care journey because the majority of their care journey will happen outside of the four walls of any health care 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, sort of 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 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. And I think that highlights the fact that patients are not actually an end user that's of consideration when it comes to traditional clinical tools. And I was a patient of a specific hospital when I lived in Boston. And it turned out when I was admitted for labor, for delivery, I had multiple records in their systems based on different instances where I had different needs and we're describing primary care and the responsibility of this notion of APCP knowing everything

about me, when that can be, number one, extremely overwhelming to know for every single part of my health care journey, which, you know, may have very different needs if I'm pregnant and going through a maternity journey versus if I get sick with COVID or anything else, you know, the type of information and the type of judgment that's necessary in each of those instances is very different. How do you sort of appropriately balance the horizontal view and the longitudinal journey of a given individual with the notion of, you know, the bundles of care, the unbundling of primary care across the different, you know, kind of mini journeys that we all have as patients. The type of data that, again, 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, you know, sort of appropriately overlay the right semantics and the right context, as Iber is saying, for that particular instance of care need? There's a lot of times where doctors are forced to, and 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. We're all used to tools like Shazam now. But trying to figure out what's wrong with the 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, right? 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 just similarly, I think you need a sequence of measurements to actually understand the story that's going on in a person's physiology and it kind of can explain where they are. You need to hear the whole song to know what it's saying. 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 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 making these transformations in healthcare that I think is a really awesome opportunity that we run to instead of running from. The other piece is around how do we, when we give people information, their ability to make those changes is also impacted around the environment and the priorities and the access that they have, whether it's to the ability to exercise or healthy foods or what their job requires. 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. Yeah, I totally agree. This information is so valuable for us just optimizing, you know, our society. That's, I think, ultimately how we get to a healthcare system that actually gets better or 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, right, they can very quickly and intuitively correlate changes in their behavior to improvements in their health or decreased risks. But they don't have that feedback right now. It also begs the question of, you know, what is the primary care provider's skill set? What does that skill set need to be in the future, right? I mean, we're almost upending, like, the very definition of, like, what is a PCP?

It's no longer just about, like, interpreting the test results or, you know, doing your basic workup. But really, it's about, like, how do you ask the right questions of the data? And it's almost like the wave of data science that occurred in general engineering and, you know, kind of computer science where, you know, the skill set became less about, like, 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? Right. that, it's almost like you can imagine another credentialed, you know, a provider type that has to exist to make all this work. And, and then, you know, what happens to kind of the traditional physician, you know, archetype of the person who's doing the real clinical interpretation

is that, you know, does that continue to exist? But, you know, 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, you know, to be able to look holistically at that patient in that context with all the information, et cetera, and then do you have sort of a separate, you know, kind of class or tier of folks who are standard in a clinical practice that are these dataists, essentially, that support that physician. If we do that, we fail to build the right tools. Like, technology should not require people that get a data science degree.

Like, doctors should, if these tools should liberate a doctor to actually make just decisions. I mean, 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 end. internet. How long did it take you to learn to use Google? I think actually that the clinical decision support tool the future liberates a doctor just ask questions and the system will give answers. It will be, you know, Dr. Ewer will say, tell me about just 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. 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. And 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. And 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. So primary care, five, 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? Yeah, I think the couple of dimensions that change are, you know, one, the notion of resource constraint that we started with, you know, 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, you know, the licensure sort of burdens and things of that sort are taken off the table. So the notion of like I have to rely on the supply within a five mile radius of my home, you know, 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, you know, kind of flipping the paradigm from one in which, like, 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, actually, Julie, you're the one who needs to come in now versus Hannah, you're fine and you can stay home for the next six months. I think that whole paradigm will flip such that, you know, we wait for the doctor to tell us what we need versus us having to, you know, put ourselves in the queue, you know, know, to figure out whether or not we even need to come in. I think that primary care doctors, just their role, if anything, is our amplified.

They're the QB of your health, the quarterbacking. They're the director. Like, 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. The 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 may be, they're definitely going to be the patient themselves. They're going to be the head coach. And then 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. That's awesome. Thank you guys so much for joining us on the A16Z podcast. And thanks, especially to all the primary care docs, being all our quarterbacks right now.