Speaker 1: Welcome to Tech Stuff, a production from iHeartRadio. Hey thereon
Speaker 1: Welcome to Tech Stuff. I'm your host, Jonathan Strickland. I'm
Speaker 1: an executive producer with iHeart Podcasts and how the Tech
Speaker 1: are you? Okay, So this isn't really tech stuff today,
Speaker 1: I thought it would do something a little different. So
Speaker 1: recently we had Jacob Goldstein on the show. And Jacob
Speaker 1: is a journalist. He's done tons of work for multiple
Speaker 1: prestigious news outlets, and he's also the host of a
Speaker 1: podcast called What's Your Problem with Jacob Goldstein? And on
Speaker 1: that podcast, Jacob talks with various smarty pants in the
Speaker 1: engineering field to talk about how technology can potentially help
Speaker 1: us solve some very difficult problems. And I thought it
Speaker 1: would be great to bring you an episode of his podcast,
Speaker 1: because I think if you dig text, you're also going
Speaker 1: to dig What's Your Problem. But I know it can
Speaker 1: be a hassle to go seek out another podcast, and
Speaker 1: a lot of y'all may never take that initiative. So
Speaker 1: I thought, well, I'll bring one episode in just for today,
Speaker 1: and we can listen to an episode of What's Your
Speaker 1: Problem and enjoy that, and then if you like it,
Speaker 1: you can go seek out that podcast and subscribe to it.
Speaker 1: And if otherwise you're like this isn't my bag, well
Speaker 1: don't worry. We'll have another tech stuff episode for you
Speaker 1: on Wednesday. So this episode is called using AI to
Speaker 1: help Doctors Save lives, and I think that's a cool
Speaker 1: topic to talk about. Often on this show, I'm talking
Speaker 1: about artificial intelligence in a rather skeptical way because I
Speaker 1: feel it's not a fault with AI necessarily. It's a
Speaker 1: fault in how lots of businesses are rushing to try
Speaker 1: and incorporate and adopt AI without fully baking in a
Speaker 1: business reason for it, and that kind of short sightedness
Speaker 1: can often have negative consequences. But I would never deny
Speaker 1: the fact that artificial intelligence does have its place and
Speaker 1: it can end up being a huge benefit to us
Speaker 1: if we design it properly and implement it properly. That's
Speaker 1: a big if, and I think in healthcare is one
Speaker 1: place where AI makes a lot of sense, again, assuming
Speaker 1: that we do take the care to design and implement
Speaker 1: it appropriately. Obviously, there are very high stakes when we're
Speaker 1: talking about healthcare. So let's listen in on this episode
Speaker 1: of What's Your Problem? And I hope you enjoy.
Speaker 2: When you walk into a hospital, technology is everywhere. In
Speaker 2: one room, a surgeon is giving a patient a bionic knee.
Speaker 2: In another room, a CT scanner is creating this incredible
Speaker 2: three D picture of the inside of a person's body.
Speaker 2: But in other places the hospital feels less high tech.
Speaker 2: Doctors are still reading patients charts and making decision partly
Speaker 2: on evidence but largely on instinct. This part of the
Speaker 2: hospital is not so different from what it might have
Speaker 2: looked like, you know, fifty years ago, and bringing new
Speaker 2: technology to this part of medicine to care at the
Speaker 2: bedside is a really hard, really interesting problem, because you
Speaker 2: not only have to figure out how to use technology
Speaker 2: to deliver useful information to the doctor at the right time,
Speaker 2: you also have to figure out how to convince the
Speaker 2: doctor that the information is actually worth listening to. I'm
Speaker 2: Jacob Boldstein and this is What's Your Problem, the show
Speaker 2: where I talk to people who are trying to make
Speaker 2: technological progress. My guest today is Succi Sarya. She's the
Speaker 2: founder and CEO of a company called Baesian Health, and
Speaker 2: she's also a professor at Johns Hopkins, where she runs
Speaker 2: a lab focused on machine learning and healthcare. Succi's problem
Speaker 2: is this, how can you use artificially intelligence to detect
Speaker 2: when hospital patients are at risk of potentially deadly complications?
Speaker 2: And then once you've done that, how can you get
Speaker 2: doctors to believe that the AI's warning is worth paying
Speaker 2: attention to. She told me she first got interested in
Speaker 2: healthcare sort of by accident, when she was a grad
Speaker 2: student at Stanford studying AI and robots.
Speaker 3: You know, I grew up actually being fascinated by AI.
Speaker 3: I loved AI, and really most of my interest was
Speaker 3: in the algorithm front and like looking at robotics and
Speaker 3: building robots that were really smart, you know. And I
Speaker 3: got acquainted with medicine through a friend colleague who was
Speaker 3: a doctor taking care of babies. And what I learned
Speaker 3: through her was that this is all this data we're
Speaker 3: starting to collect, but literally nobody was doing designing any
Speaker 3: software to make sense of it. So it was just
Speaker 3: coming from a world where you know, I studied all
Speaker 3: kinds of data day in day out, with robots doing
Speaker 3: fun tasks like getting the robot to hold the ball
Speaker 3: or juggle the ball to then realizing, holy crap, there's
Speaker 3: like so many more useful things we could be doing.
Speaker 3: So that was really my first discovery of like how
Speaker 3: big a gap there was between people who thought about
Speaker 3: AI versus people versus the problems that needed to be solved,
Speaker 3: and how little we understood about these problems.
Speaker 2: So so you decide that this is going to be
Speaker 2: your thing, right, this is your life's work now.
Speaker 3: I mean in the beginning, I wasn't convinced. In the beginning,
Speaker 3: it was just about spending a few years helping out
Speaker 3: and making sure we are able to make you know,
Speaker 3: in the beginning, it was about my next three years.
Speaker 3: Like I was afraid of investine. I was afraid of
Speaker 3: the complexity of medicine. Like it wasn't an easy field.
Speaker 3: It's not one where they welcome you, right, just as
Speaker 3: an engineer, you don't come in and like at least
Speaker 3: twelve thirteen years ago, that wasn't the culture that.
Speaker 2: Like right, Like like an an MD a hospital does
Speaker 2: not want to hear from some AI researcher. They're busy,
Speaker 2: Oh no.
Speaker 3: For sure, and they're like, we're busy, we have real
Speaker 3: work to do.
Speaker 2: Yeah, what is this?
Speaker 3: Like this all sounds an esoteric mumbo jumbo.
Speaker 2: Yeah, And so you say, you know, we're collecting all
Speaker 2: this data in healthcare and we're not doing anything with it.
Speaker 2: That is not intuitive, Like, that's not you know, I
Speaker 2: think most people sort of prior And this is at
Speaker 2: an academic hospital, right, Your friend is at Stanford Hospital,
Speaker 2: a very prestigious academic hospital. I think Stanford Hospital, I
Speaker 2: think data. I think these are people doing research. So
Speaker 2: what do you mean when you say we're collecting all
Speaker 2: this data and not doing anything with it.
Speaker 3: Yeah, So twelve thirteen, fourteen years ago, this field was
Speaker 3: very new and at the time even collecting and storing
Speaker 3: this data, natural question was can be afforded? It costs
Speaker 3: dollars to store this data? Why would we do that?
Speaker 2: And when you say, what what kind of data are
Speaker 2: you talking about here? When you say collect and store
Speaker 2: this data?
Speaker 3: So literally, this was at the time babies entering, you know,
Speaker 3: in the new natle ICU, these are premature babies are
Speaker 3: born in real time. Devices are collecting heart rate and
Speaker 3: vitals and oxygen saturation data and like, and so that
Speaker 3: kind of detailed data, which is much more bulky, was
Speaker 3: historically not stored. Instead, what they would do is they'd
Speaker 3: take like fifteen minute averages and capture that okay, And
Speaker 3: naturally the question came up, do we need to store it?
Speaker 3: This is really expensive data. Let's just throw it away
Speaker 3: after forty eight hours, we don't need it anymore. Let's
Speaker 3: just throw a quick summary of it.
Speaker 2: Huh. So you might do a study, you might track
Speaker 2: certain data points, but the idea that you're going to
Speaker 2: just as a matter of course, be storing all of
Speaker 2: this data that is now being generated and saved because
Speaker 2: electronic medical records are just being adopted. Nobody was doing that.
Speaker 2: Nobody had really thought to do it. It was an
Speaker 2: expensive prospect. It didn't seem like there would be a
Speaker 2: good reason to do it exactly.
Speaker 3: And coming from AI, where we looked at you know,
Speaker 3: fingerprint data on the internet in retail or finance, then
Speaker 3: the you know, we it was so natural to think
Speaker 3: about how this data teaches you things that it felt
Speaker 3: crazy to me that like me one similarly, all sorts
Speaker 3: of amazing things about these babies or human body or
Speaker 3: how we're involved, or like what are the signs and
Speaker 3: fingerprints of disease? How did they show up?
Speaker 2: When you say fingerprint data, that's a that's a metaphor, right,
Speaker 2: what does fingerprint data mean? In the context of sort
Speaker 2: of e commerce and online finance.
Speaker 3: Well, like they went to this site and then they
Speaker 3: came to this site, or like they saw and add
Speaker 3: somewhere else about this, and now you know they're searching
Speaker 3: for something, and it shows you intense It's.
Speaker 2: This moment ten years ago when like the when people
Speaker 2: are using data to know like everything about what I
Speaker 2: do when I'm shopping for new shoes. But you you're
Speaker 2: but they're not collecting data on like sick newborn babies
Speaker 2: exactly right.
Speaker 3: Does that mind blowing to you? Because it was crazy
Speaker 3: mind blowing to me.
Speaker 2: Okay, yes, my mind is blown. So what do you do?
Speaker 3: Well? I mean it seemed like such a pressing problem.
Speaker 3: It also helped that we were funded as a moonshot
Speaker 3: project by the Google founders, that it was a high
Speaker 3: profile investment, and it sort of naturally led way for
Speaker 3: United place like Stanford curiosity, and we had some amazing
Speaker 3: collaborators who were equally curious, who said, well, let's dive
Speaker 3: in and see what we'll understand. And that was the
Speaker 3: start of it. I literally got hold of this massive,
Speaker 3: twelve hundred page, like this huge thig book to learn
Speaker 3: about babies and what conditions they experience and what does
Speaker 3: it all mean, and then starting to understand how does
Speaker 3: it show up in the data, and you know, spent
Speaker 3: evenings and weekends, and actually I remember sitting in the
Speaker 3: basement of Stanford Hospital at over Christmas trying to work
Speaker 3: on trying to get data out of the health record
Speaker 3: in the first place. And we were trying to experiment
Speaker 3: with all of techniques for pulling the data out, which
Speaker 3: you know now is a whole lot easier than it
Speaker 3: was twelve years ago because.
Speaker 2: It's not built for that, right, It's basically built somewhat
Speaker 2: to track the patient and to a significant degree to
Speaker 2: like bill insurance. Right, that's traditionally what electronic medical records
Speaker 2: were for.
Speaker 3: That's exactly right.
Speaker 2: Kind of amazing and kind of weird. I mean, I
Speaker 2: want to talk more about the bigger idea of data
Speaker 2: and healthcare, but just to kind of land this moment
Speaker 2: early in your career at Stanford, like, is there some
Speaker 2: project you do, Like what is the end of your
Speaker 2: work at Stanford.
Speaker 3: So the project was, you know, we're monitoring these premature
Speaker 3: babies right anywhere between twenty four week old babies which
Speaker 3: are very very tiny, like very twenty.
Speaker 2: Four weeks of gestation.
Speaker 3: To be exactly to like twenty eight thirty thirty two.
Speaker 3: And the idea was, these babies, you know, are like
Speaker 3: they're at risk for significant, like an array of complications. Yeah,
Speaker 3: and the idea is the sooner you know, the earlier
Speaker 3: you can do something about it, the greater the chance
Speaker 3: that you're going to actually resuscitate them. So our job was, like,
Speaker 3: could we look at this data from the second they're
Speaker 3: born and collect this data to start analyzing and modeling
Speaker 3: which babies at risk for which of these complications? And
Speaker 3: if you could, then you could start to put more
Speaker 3: of these preventative prophylactic type pathways or approaches in place
Speaker 3: for carrying.
Speaker 2: Basically identify problems more quickly leading to better outcomes. That's
Speaker 2: the basic desire exactly.
Speaker 3: And in the process I discovered, like, you know, a
Speaker 3: long time ago, there was a physician named Virginia Apgar,
Speaker 3: and what she figured out is like, just by measuring
Speaker 3: five different things from when the baby is born, she
Speaker 3: can compute a very simple score that tells you how
Speaker 3: the baby's doing. And so so naturally, the question we
Speaker 3: asked is, Okay, so now that we are seeing all
Speaker 3: these ways in which the machine learning and AI is
Speaker 3: discovering novel signs and patterns are predictive. Could we just
Speaker 3: simply combine this to come up with a simple score
Speaker 3: that says, you know, can I predict complications? And what
Speaker 3: we found was this new simple score that uses data
Speaker 3: that no special thing you have to do, it's already
Speaker 3: being collected. We just analyze it and we ought to
Speaker 3: compute the score turns out to be much more predictive
Speaker 3: than the ABGAR at predicting complications.
Speaker 2: And so so it worked. I mean, did do people
Speaker 2: use it? Is it standard of care? Now? What happened
Speaker 2: with that? With that research?
Speaker 3: So at that point I was like, oh, this is
Speaker 3: so cool. And literally we got all these journalists who
Speaker 3: wanted to write about it, and it was on the
Speaker 3: fundraising you know, it was like Stanford's fundraising highlight for
Speaker 3: like the next five years, et cetera. But what was
Speaker 3: the saddest thing about it is that there was no
Speaker 3: natural mechanism for implementing it in practice. And it had
Speaker 3: to do with so many different pieces to it, Like
Speaker 3: we didn't have the infrastructure, we didn't have the like
Speaker 3: know how of like how do you get physicians to
Speaker 3: trust something like this. How do you build this in
Speaker 3: a way that is true, us worthy and reliable. How
Speaker 3: do you do this so that it's not just like
Speaker 3: a pet project in one hospital, but it's like a
Speaker 3: system that is scalable nationally. And you know, what is
Speaker 3: the incentive structure? Who pays for it and why would
Speaker 3: they pay for it? And all of that is literally
Speaker 3: what sort of got me, like got me super interested
Speaker 3: in the field where I started to feel, Wow, we're
Speaker 3: at the start of what feels like is a massive movement,
Speaker 3: has many components to be figured out, but we need
Speaker 3: to figure this out. Interestingly, at the time on sand
Speaker 3: Hill Road, you know why, virtually being in pal Aalto.
Speaker 2: Yes Santel Road where all the venture capitalists are exactly.
Speaker 3: People were like, this is fantastic, here's money. Why don't
Speaker 3: you start a company on this topic? And I spent
Speaker 3: six months investigating, you know, talking to lots of peers
Speaker 3: health systems, hospitals and realizing we're just too early. There's
Speaker 3: a lot of work that needs to go in place
Speaker 3: for this to become something that will scale nation. Now,
Speaker 3: fast forward ten years.
Speaker 2: Later, I want to fast forward, but give me just
Speaker 2: another moment when you say it's too early, Like in
Speaker 2: what ways was it too early? Like specifically, what was
Speaker 2: not not ready in the world to start a company
Speaker 2: at that time?
Speaker 3: So the first thing we needed is for hospitals to
Speaker 3: be ready to implement a system like that. For that
Speaker 3: to happen, they needed to have implemented the Electronic Health record, huh,
Speaker 3: be stable users of the HR so that they'd be
Speaker 3: willing to plug in third party systems on top of it.
Speaker 2: And it's kind of amazing that ten years ago, you know,
Speaker 2: twenty whatever, twenty teens, still hospitals were not sort of
Speaker 2: ubiquitous users of electronic medical records, right, like doctors were
Speaker 2: still writing on paper.
Speaker 3: Honestly, coming from computer science where I did you know,
Speaker 3: where I was involved in other areas of AI and
Speaker 3: computer science, like this was like the biggest like shift
Speaker 3: in mindset I felt every time I came back into
Speaker 3: the healthcare side of the equation, it felt like I
Speaker 3: was going at least twenty thirty years back, right.
Speaker 2: Like get a time machine going into the past when
Speaker 2: you walk into the hospital, which is particularly, I don't know,
Speaker 2: ironic surprising, given how in some ways healthcare feels very
Speaker 2: cutting edge, right, Like A central interesting thing to me
Speaker 2: about the work that you do is the way in
Speaker 2: which healthcare is. You know, you go get a whatever,
Speaker 2: a CT scan. It's this incredible machine and it uploads
Speaker 2: to a computer and a whatever AI radiologist can you
Speaker 2: know read the scan blah blah blah. And yet on
Speaker 2: the kind of data side, on the complicated patient at
Speaker 2: the bedside side, it's still very kind of old fashioned
Speaker 2: and almost artisanal.
Speaker 3: I mean, you raise like a fantastic point, which is
Speaker 3: I think when it comes to introducing and designing new medicines, Yeah,
Speaker 3: we've become really really good, but in terms of once
Speaker 3: the medicine is produced, in terms of actually accelerating the adoption,
Speaker 3: optimizing the update, yeah, designing who gets it and what
Speaker 3: does and when detecting early who would benefit from it.
Speaker 3: That's what I call the healthcare delivery side of the equation.
Speaker 3: I feel like there's a very very vast gap of
Speaker 3: what needs to happen to get better.
Speaker 2: So, okay, so you do this project. You see that
Speaker 2: it's too early to start a company because the world
Speaker 2: isn't ready yet, because hospitals aren't even widely using electronic
Speaker 2: medical records yet. Much less being ready to sort of
Speaker 2: expert the data and listen to the data, et cetera.
Speaker 2: And you take a job as a professor at Johns Hopkins, Right,
Speaker 2: is that the next step?
Speaker 3: That's right? And part of the move to Hopkins was
Speaker 3: realizing there's so much depth and breadth of medicine, not
Speaker 3: just around the on the actual devices or the engineering
Speaker 3: on the chemical or the drug development, but also on
Speaker 3: the delivery side, like how what does it take to
Speaker 3: scale ideas nationally? How do you design policy around it?
Speaker 3: There was sort of a whole institute dedicated to scaling
Speaker 3: ideas nationally, So to me that was extremely exciting to
Speaker 3: learn about what would it take to really build the
Speaker 3: foundations of a field like this. And moving to Baltimore
Speaker 3: was a big move, but I was just excited by
Speaker 3: the idea of learning it all and learning it especially
Speaker 3: as an engineer as ERNII research, as an outsider coming
Speaker 3: into healthcare.
Speaker 2: In a minute, Succi and her colleagues figure out how
Speaker 2: to use AI to detect when certain patients are at
Speaker 2: risk for complications and also how to get doctors to listen.
Speaker 2: So Succi is at Johns Hopkins in Baltimore and she
Speaker 2: has this big idea using AI to help doctors treat
Speaker 2: hospital patients, but she has to figure out exactly what
Speaker 2: to focus on.
Speaker 3: One of the big areas was this idea of like
Speaker 3: early detection of patients at risk for complications and diagnostic
Speaker 3: errors being the third leading cause of death. Like that's nuts. Like,
Speaker 3: so today, you know there are critical moments that are missed.
Speaker 3: We get patients the wrong diagnosis or that they're developing
Speaker 3: something subtly and slowly. That's like a whole branch of
Speaker 3: diagnostic errors where you know, complication or a condition develops,
Speaker 3: but they don't get noticed in a timely fashion. And
Speaker 3: so these seemed perfect for AI to come in with
Speaker 3: the kind of data that exists to be able to
Speaker 3: flag patients that are high risk and make it easy
Speaker 3: to provide a second pair of eyes.
Speaker 2: Because it's basically pattern matching, right, I mean, differential diagnosis
Speaker 2: is taking lots of different variables from the patient and
Speaker 2: trying to put those variables together to match the patient
Speaker 2: to you know, thousands of other patients and say, oh,
Speaker 2: all of these, all of these variables, all of these
Speaker 2: health indicators suggest that the patient has disease X. Like
Speaker 2: that's fundamentally what a differential diagnosis is, and like machine
Speaker 2: learning should be very good.
Speaker 3: At that exactly. And previously people have attempted differential diagnosis
Speaker 3: with very coarse symptoms, like high level description of like
Speaker 3: you have cop your fever. What was different this time
Speaker 3: around is because of the HR, we had very detailed.
Speaker 2: Data the EHR, the electronic health record right exactly.
Speaker 3: And so it provided this brand new opportunity to do this.
Speaker 3: And then you know, naturally when you go down the
Speaker 3: list and start looking at problem areas, sepsis is a
Speaker 3: model disease. We chose to demonstrate the idea.
Speaker 2: So let's just talk about sepsis for a minute. What
Speaker 2: is sepsis?
Speaker 3: So let's say your patient gets infected. Your immune system
Speaker 3: is now going to do respond in order to protect
Speaker 3: your body, but in sepsis, it overreacts and starts attacking
Speaker 3: your organ systems, leading to organ failure and depth. And
Speaker 3: so the idea of its sepsis treatment is very much
Speaker 3: the earlier you can detect it, the better you are
Speaker 3: at like tackling it.
Speaker 2: Right, Okay, so I buy it. It seems seems like
Speaker 2: a big problem and it seems like one that might
Speaker 2: be solved or at least, you know, made less bad
Speaker 2: by with the application of machine learning. So how do
Speaker 2: you how do you actually do it? What do you
Speaker 2: have to do to build the model and see if
Speaker 2: it works and get people to use it.
Speaker 3: Yeah, so this is almost like what you're about to
Speaker 3: describe in two minutes what was almost a five year journey.
Speaker 3: So first, it's collecting a huge amount of data where
Speaker 3: you can identify both patients of suptic versus non septic
Speaker 3: and when they had it, and what other conditions did
Speaker 3: they have, and what else was happening in their life right,
Speaker 3: and you know, all the data leading up to that
Speaker 3: episode and what was done after the fact. So you
Speaker 3: get the data. Then the next part is, you know,
Speaker 3: you have to actually understand the biological process or the
Speaker 3: clinical process that's happening and layer that on top of
Speaker 3: the data to make sure you're going from like just
Speaker 3: bits and bytes to data that makes sense, okay, And
Speaker 3: then you implement lots of different learning algorithms to be
Speaker 3: able to experiment, you know, the thing that we first
Speaker 3: did versus the thing we do now. There's like lots
Speaker 3: of generations of improvements in order to get to a
Speaker 3: place where you're going from like, you know, not very
Speaker 3: good signal to very good signal.
Speaker 2: So you're building a model through trial and error, basically
Speaker 2: trying to get an AI model that has a high
Speaker 2: sensitivity and specificity that's good at issuing an alert when
Speaker 2: a patient has sepsis, and does an issue too many
Speaker 2: alerts when the patient doesn't have sepsis.
Speaker 3: Basically exactly, and also does it in a way that
Speaker 3: you know, when it says somebody has sepsis, it's able
Speaker 3: to explain why. It's able to provide enough information so
Speaker 3: that the clinician can act on it. And it's not
Speaker 3: doing it solely that there's not enough to work on,
Speaker 3: and it's not doing it so late that it's useless.
Speaker 2: Like often people talk about AI models machine learning models
Speaker 2: as black boxes, right, like, very good at pattern matching,
Speaker 2: very good at predicting the next word, but we don't
Speaker 2: know why, And so you're saying in this instance, you
Speaker 2: sort of need to know why.
Speaker 3: My very key evolution of a scientist working in this
Speaker 3: area was in the beginning, I saw it all as
Speaker 3: data in math, and then as I started working more
Speaker 3: and more in interfacing and actually deploying systems like this,
Speaker 3: what I started realizing it's actually not math and data,
Speaker 3: it's about trust, because ultimately, to get adoption and to
Speaker 3: get outcomes, I need to get trust from these highly
Speaker 3: trained clinicians who studied this year and year out, and
Speaker 3: they have a process in a system for working and
Speaker 3: you have to fit within this system.
Speaker 2: And they're very busy, and it's very high stakes, and
Speaker 2: they kind of think they know everything, and it's so
Speaker 2: presumably very hard to get them to trust you in
Speaker 2: making their clinical judgments exactly.
Speaker 3: But moreover, I've also been on the other side of
Speaker 3: like tons of engineers making all sorts of about their
Speaker 3: system knows better, but when you actually go and make
Speaker 3: sense of what the evaluations they've done, they literally have
Speaker 3: very little understanding of medicine and the practice of healthcare,
Speaker 3: so like their claims are mostly not good. So a
Speaker 3: huge part of it is like developing respect and humility
Speaker 3: for the system, the complexity, so that when you're bringing
Speaker 3: in this new thing, it really truly fits, it's easy
Speaker 3: to use, it makes sense, it creates value. Without all that,
Speaker 3: you're not going to get to the benefit.
Speaker 2: So now you say it creates value, and suddenly you
Speaker 2: sound like a founder, an entrepreneur and not like an
Speaker 2: academic where where in this arc do you start a company?
Speaker 3: You know, it was somewhere in twenty eighteen. I remember
Speaker 3: twenty eighteen was a transformative video for me for a
Speaker 3: number of reasons. I'll start with the very simple thing
Speaker 3: of like, when we first built this system and deployed it,
Speaker 3: only like two or three clinicians use it, and it
Speaker 3: was the two to three clinicians who were involved in
Speaker 3: working on the project with us. What I realized was
Speaker 3: we knew from looking at large amounts of data that
Speaker 3: the system was working, it was working correctly, and we
Speaker 3: could identify these cases. We could identify them early, and
Speaker 3: even from interacting the clinicians, we knew you could do
Speaker 3: something differently about it. So it's one thing for system
Speaker 3: to detect. You know, clinicians will say, so what, so
Speaker 3: what am I supposed to do well about it? And
Speaker 3: in this scenario, we've even done studies to know that
Speaker 3: actually they could be acting, you know, they could use
Speaker 3: this output to meaningfully change the patient's care. So then
Speaker 3: to me, the question was, Okay, if we know this
Speaker 3: thing works, why the heck are we not succeeding? And
Speaker 3: that's kind of where it went from the puzzle of
Speaker 3: math and data to trust. You know, how do we
Speaker 3: develop and deploy it in a way that's transparent. How
Speaker 3: do we understand like what are the top of mind
Speaker 3: issues from a practicing clinician's point of view, and how
Speaker 3: do we address it? Where are we creating value? How
Speaker 3: do we start quantifying value?
Speaker 2: There any moments where you're like, you know, you have
Speaker 2: this thing that can be helpful, and yet someone a doctor,
Speaker 2: a hospital administrator, whatever, is telling you why they're not
Speaker 2: going to use it.
Speaker 4: Basically, I mean so many moments I can't even like
Speaker 4: begin so I think I remember this time when they
Speaker 4: basically were like, Okay, this thing is flagged the system.
Speaker 3: What do I do with it? And I was like,
Speaker 3: you should look if the patient has something, And they
Speaker 3: were like, are you kidding me? How many flags?
Speaker 1: Do you know?
Speaker 3: How many alerting systems exist? If I were to take
Speaker 3: every single alerting system and start to use that to
Speaker 3: start informing when I'm doing a diagnostic workup and what
Speaker 3: am I doing, I basically would not get my day
Speaker 3: to day work done right.
Speaker 2: It's like it's like when you're if you're ever in
Speaker 2: an emergency room, like everything is beeping all the time,
Speaker 2: and your system is just one more beep in a
Speaker 2: sea of beeps that everybody ignores, and.
Speaker 3: You feel passionately about it.
Speaker 2: Yeah, it's your reasons you care about this beep, but
Speaker 2: nobody else cares about this.
Speaker 3: Being Nobody gives a damn. And it was just like
Speaker 3: so it was difficult, right, like you come. I was
Speaker 3: sort of like, you know, I felt defeated. I sat there,
Speaker 3: I was like, this is so unbelievable. This is like
Speaker 3: so powerful. Why aren't they believing me? And so there
Speaker 3: was an information gap right like then it was like understanding,
Speaker 3: oh this you know, the system in which they live. Okay,
Speaker 3: I understand that all these different alerts exist. How are
Speaker 3: these alerts created? How are we different? How can we
Speaker 3: demonstrate we're different? Why should we be trusted? And so
Speaker 3: that was as an example starting point. Like another one
Speaker 3: was like we deployed it, and we deployed it in
Speaker 3: a way where it was you know, within the electronic
Speaker 3: health record, but it was done in a way that
Speaker 3: was really cumbersome, like every time they needed to respond,
Speaker 3: it was like a few you know, it was like
Speaker 3: a minute and a half of work, and you know, honestly,
Speaker 3: they're so busy. A minute and a half extra to
Speaker 3: do something that they don't already have total conviction in
Speaker 3: is like a lot to So then you spend a
Speaker 3: bunch of time optimizing, well, how do we go it
Speaker 3: from me take it from a minute and a half
Speaker 3: to like three seconds? How do we optimize it so
Speaker 3: that it's instantaneous? It's easy, it's just there.
Speaker 2: So this isn't about the data at all. This is
Speaker 2: just user experience basically.
Speaker 3: Hugely human factors, like human factors and human factors here
Speaker 3: is very different and complicated because you're trying to optimize
Speaker 3: human factors within a chassis that is very complicated. Right,
Speaker 3: Like you're not like standalone software, This is like you're
Speaker 3: within an electronic health record, and like, how do you
Speaker 3: do this in a way that the electronic health record
Speaker 3: providers will allow.
Speaker 2: You information not your software?
Speaker 3: Yeah, it's not your software, And how can you do
Speaker 3: it in a way that is smooth and seamless and
Speaker 3: they actually like it? And then you can do this
Speaker 3: in a way where it's not just custom built for
Speaker 3: a Johns Hopkins, but it's something that you can send
Speaker 3: to take to a rural hospital, right.
Speaker 2: So you're doing all this, at what point in this
Speaker 2: arc do you start the company?
Speaker 3: So another like personal thing happened, which is I lost
Speaker 3: my nephew to sepsis. And you know, it was the craziest,
Speaker 3: like saddest, like you know, most insane feeling to be
Speaker 3: able to like, you know, as like a researcher, as
Speaker 3: a scientist. I'm like ned deep in these research areas.
Speaker 3: And then it's one thing to go and talk about it,
Speaker 3: to say, well, here's how you do it, and here's
Speaker 3: how it works, and here's why it will work, and
Speaker 3: here's why this is a great idea. And it's another
Speaker 3: to then come to that moment of realization where like,
Speaker 3: well I haven't actually done anything to make a difference.
Speaker 2: So you're already working on sepsis, yes, and your nephew
Speaker 2: you say, nephew meaning younger than you?
Speaker 3: Is this a young much younger than me?
Speaker 2: Wow?
Speaker 3: And realizing like I was doing, like it all sounded
Speaker 3: like an excellent like it all sounded great on paper,
Speaker 3: you know it. It was like, you know, I'd go
Speaker 3: to meetings and lots of people would listen and they'd say, yay,
Speaker 3: great idea, et cetera. But then at the end of
Speaker 3: the day, for me, it was like I'd gotten too
Speaker 3: used to you know, it's easy. It's easy to like
Speaker 3: talk about something smart and then people say it's a
Speaker 3: great idea, and then you leave the room and you
Speaker 3: feel good about it, and then you go back and
Speaker 3: you work on it some more. And I think it
Speaker 3: was hard, like hard for me to sort of realize
Speaker 3: like I had gotten to carre it away and I'd
Speaker 3: gotten to carre it away like not thinking about what
Speaker 3: is it actually going to take to make it real?
Speaker 3: And the making it real is what's like just so
Speaker 3: much harder than I thought. But part of it is
Speaker 3: I also felt like this isn't just a sad This
Speaker 3: isn't just like a you know, for an idea for sepsis.
Speaker 3: This is really like crazy to me that this isn't
Speaker 3: how we operate the like I think the time has
Speaker 3: come and what is exciting to me is in the
Speaker 3: last year or two, I'm starting to see the world
Speaker 3: has shifted. There's been a very meaningful change in the
Speaker 3: last few years. I think losing my like losing my nephew,
Speaker 3: made it very real. It went from this idea to
Speaker 3: feeling like this was an opportunity where it's very real.
Speaker 3: Now we can make a difference. The pieces exist, and
Speaker 3: I need to make it happen. I can't hide anymore.
Speaker 3: And in twenty eighteen I went from like started to
Speaker 3: realize like most systems that finished implementing the health record,
Speaker 3: electronic health record policies were starting to change. The AI
Speaker 3: was mature enough that it was really clear we could
Speaker 3: do a lot with it. And it was my very
Speaker 3: little part I could do to you know, address my
Speaker 3: my you know, my part of grief related to my nephew.
Speaker 3: Like it was the very little role I could play.
Speaker 3: So so in twenty eighteen I started to, you know,
Speaker 3: think go after it with the idea of the work
Speaker 3: to actually start a company. We're actually going to turn
Speaker 3: this into something that scales nationally. And that's where it
Speaker 3: all began.
Speaker 2: So you start the company, and you do build this
Speaker 2: AI model to detect sepsis in the hospitalized patients, and
Speaker 2: you do this study and you wind up publishing the
Speaker 2: outcome in the journal Nature Medicine, right, which seems like
Speaker 2: a big, big moment in your work, in the life
Speaker 2: of your company. So tell me about that study.
Speaker 3: Yeah, So in twenty two in July twenty two, we
Speaker 3: had three studies. They were featured on the cover of
Speaker 3: Nature Medicine. These were very big studies for the field.
Speaker 3: Then the studies that came out in twenty two were
Speaker 3: basically showing how we implemented the system by five different sites,
Speaker 3: like both in the emergency department, the floor, the hospital
Speaker 3: flow is the ICUs across academic and community hospital, So
Speaker 3: five different hospital in totally different geographic region right in
Speaker 3: Maryland in DC, rich communities, poor communities. And what we
Speaker 3: were able to show was the system both like you know,
Speaker 3: almost three quarter of a million patients in the study
Speaker 3: forty four hundred physicians and nurses who were part of
Speaker 3: the study that you could detect sepsist significantly earlier than
Speaker 3: they were currently detecting and acting on. So that was
Speaker 3: one second we showed that. In fact, when we then
Speaker 3: implemented the system, we show saw meaningful reduction in treatment timing,
Speaker 3: like patients were getting treatment in a more timely fashion
Speaker 3: when providers were seeing the alert and acting off of it.
Speaker 3: And then the third we know early detection is possible
Speaker 3: now and we know treatment timing has moved, and we've
Speaker 3: known in sepsis that early treatment is the key to
Speaker 3: better outcomes, So the questions do we see that in
Speaker 3: our population as well? And we saw that in patients
Speaker 3: who actually got you know, early alerts. On who got
Speaker 3: the alerts and providers acted on it, we actually saw
Speaker 3: much better outcomes in terms of reductions in mortality, morbidity,
Speaker 3: length of state, fewer complications, secondary complications that arise out
Speaker 3: of sepsis. So it was just extremely exciting to see
Speaker 3: that we could go from you know, a technical idea
Speaker 3: to actual outcomes. And then one of the most interesting
Speaker 3: things we'd studied here was adoption. Will clinicians adopt? It
Speaker 3: was a very real world study to show, like, can
Speaker 3: of system like this actually work? And we showed ninety
Speaker 3: percent physician adoption. So that was extremely exciting to see.
Speaker 3: And that's what I call that's what you know was
Speaker 3: about closing the trust gap.
Speaker 2: So, okay, so you published this paper whatever a year
Speaker 2: and a half ago, where are you now? What's your
Speaker 2: company doing?
Speaker 3: One thing that's very also that I didn't cover earlier
Speaker 3: is that we expanded the system dramatically from not just
Speaker 3: working on sepsis, but a variety of other conditions like sepsis,
Speaker 3: where there is very significant both clinical benefit but also
Speaker 3: financial benefit for the health system. The reason the financial
Speaker 3: piece matters is, you know, ultimately health systems are working
Speaker 3: on one two percent margin. For them to be able
Speaker 3: to implement systems that actually improve care, they still need
Speaker 3: to be able to financially justify that this can be done,
Speaker 3: and that was crucial.
Speaker 2: So what are some of the other things you're working
Speaker 2: on besides sepsis? Now?
Speaker 3: Like, another example area is presh ulcers. Okay, huge area where.
Speaker 5: Like bed bed source exactly Like, it's an area where
Speaker 5: again huge patient impact in terms of like you know,
Speaker 5: if you do end up getting a serious beds or
Speaker 5: how detrimental it is for the patient, sometimes leading to death,
Speaker 5: sometimes leading the need for amputation.
Speaker 3: But even more interestingly, huge burden on the caregivers themselves,
Speaker 3: like nurses today have to do a huge amount of
Speaker 3: work to take care of these patients. Like today, there
Speaker 3: are lots of scenarios where these patients are missed, and
Speaker 3: there's an opportunity where you can actually use the data
Speaker 3: to identify this higher school and start again implementing these
Speaker 3: new ways in which you can do targeted you know,
Speaker 3: preventative measures.
Speaker 2: What has to happen for you to you know, for
Speaker 2: your software to get adopted at hospitals all around the country.
Speaker 2: Like I buy that it's helpful. How do you get
Speaker 2: from it being a kind of researchy thing to being
Speaker 2: a thing that everybody uses.
Speaker 3: So the hurdles we needed to cross was one. We
Speaker 3: needed to figure out a way to get approvals from
Speaker 3: the electronic health records to be able to integrate it.
Speaker 3: We did.
Speaker 2: That took a couple of years from like the just
Speaker 2: the big software makers, Epic, whatever, the companies that make
Speaker 2: the electronic health records. They have to say yes, okay,
Speaker 2: so that's done. Check. Great, what has to happened next? Yeah?
Speaker 3: Next, you need a system that is able to you know,
Speaker 3: when you go from one side to the next, to
Speaker 3: the next to the next. You need the ability to
Speaker 3: be able to measure and generalize as you core, cross
Speaker 3: site and reliably perform.
Speaker 2: So it has to work in lots of different kinds
Speaker 2: of hospitals that collect different kinds of data in different settings.
Speaker 3: And in our partnerships shown that data.
Speaker 2: Okay, third check.
Speaker 3: Like I said, we have to show that basically people
Speaker 3: will adopt in these different environments. So we have data
Speaker 3: to show that okay.
Speaker 2: Four.
Speaker 3: In some of these areas you need FD approval, and
Speaker 3: in the areas we need of the approval, we're working
Speaker 3: with the FDA to get those approvals.
Speaker 2: Okay. So that's kind of the next step, correct.
Speaker 3: And then once that's done, you can now start to
Speaker 3: you know, it's available, it can be marketed, you can
Speaker 3: scale it nationally. All very exciting things.
Speaker 2: So if things go well for you, what will the
Speaker 2: world look like in say five.
Speaker 3: Years, Oh my god, so exciting. I think we will
Speaker 3: actually be implemented at sixty seventy eighty percent of the market,
Speaker 3: I hope in the US. What's interesting now is like,
Speaker 3: you know, healthcare is a market which is a leader
Speaker 3: follow up market, and once you show things that work,
Speaker 3: it makes logical sense. You have the proof points, you've
Speaker 3: tackled most of the and issues that people struggle with.
Speaker 3: Then this is an area where you can scale. And
Speaker 3: when it comes to like the areas we're working in,
Speaker 3: which is clinical, unlike some of the others like billing
Speaker 3: and messaging and back office. You know, the years of
Speaker 3: development required to build what we build is very long,
Speaker 3: Like it's taken us eight to nine years to do
Speaker 3: all the pieces necessary to get to where we are,
Speaker 3: so there aren't as a lot of like other competitors
Speaker 3: in the market.
Speaker 2: You have a moat, and FDA approval is going to
Speaker 2: be even more of a.
Speaker 3: Mote among other things. Exactly, So we have a very
Speaker 3: very significant like moat and hurdles people have to cross
Speaker 3: to really get it to work, and we've invested in them.
Speaker 2: And so in your happy five year future, most of
Speaker 2: the hospitals in the country will be using your software,
Speaker 2: your models to detect sepsis, to detect bedsores earlier than
Speaker 2: in a.
Speaker 3: Variety of for the conditions. Like we've looked at our
Speaker 3: own financial models and show that like a you know,
Speaker 3: modest four to five hospital health system stands to gain
Speaker 3: like fifty two hundred million dollars from the implementation of
Speaker 3: our system in some you know, the condition areas we're tackling.
Speaker 2: And people will die less and be less sick as
Speaker 2: a benefit also.
Speaker 3: And that is honestly the biggest maturity I've had in
Speaker 3: building this company. I started from like the cause of caring,
Speaker 3: and it was realizing, like it's funny in healthcare, they're
Speaker 3: so used to caring for patients who are dying every day.
Speaker 3: They've gotten desensitized. You then come back to realizing you
Speaker 3: need the other things to follow, like the money. You
Speaker 3: need to figure out a way to make it easy
Speaker 3: for them to do the right thing, And when you
Speaker 3: do that, then they do actually care about doing the
Speaker 3: right thing, because that's why they were there in the
Speaker 3: first place.
Speaker 2: We'll be back in a minute with the lightning round. Okay,
Speaker 2: I'm going to keep you another two minutes or something
Speaker 2: to do a lightning round. You went to college at
Speaker 2: Mount Holyoke and all women's college. Yeah, and so I'm curious,
Speaker 2: what is one thing you would tell someone considering attending
Speaker 2: an all women's college.
Speaker 3: Oh? I loved Mount holy O. It was so much fun.
Speaker 3: It's where I got my confidence that I could do
Speaker 3: really really hard things and not be, you know, not
Speaker 3: feel defeated.
Speaker 2: If you weren't working in healthcare, where would you be
Speaker 2: trying to apply AI?
Speaker 3: Oh my god, I've just been so obsessed with healthcare
Speaker 3: for the last decade. I haven't really lifted my head
Speaker 3: to think about other things. I mean, honestly, there are
Speaker 3: a million areas you could apply it, but I don't
Speaker 3: like thinking about it because it's just that the need
Speaker 3: is so dire in health care, and it's so hard.
Speaker 3: It's so hard for an II research to focus in
Speaker 3: healthcare because they don't make it easy. You can make
Speaker 3: a lot more money doing the same kind of things
Speaker 3: in finance. You can get the data more easily, you
Speaker 3: can make money off of it more easily. Like it
Speaker 3: is annoying, It is really annoying.
Speaker 2: Is chet GPT overrated or underrated?
Speaker 3: Actually I think it's underrated.
Speaker 2: Okay, go on, I think you know.
Speaker 3: When we see the math, we're like, okay, that's the math.
Speaker 3: That's interesting to me. What was really informative was like
Speaker 3: the experience, the social experience. It was so exciting to
Speaker 3: see people who first interacted with it and you know,
Speaker 3: have their head mind be blown by the experience. And
Speaker 3: that's sort of then informing how important the user experience
Speaker 3: out of the houses, Like you know, we had some
Speaker 3: of the chatbot technology before we had some of the
Speaker 3: interactive but it's sort of how opening I designed it
Speaker 3: in the use cases like storytelling, poems, like the use
Speaker 3: cases where they trained the system to be very good
Speaker 3: at conversant like was what made the experience so exciting
Speaker 3: because then people could start you know, like experiencing it
Speaker 3: themselves and that sort of opened up their mind to
Speaker 3: what else could it do?
Speaker 2: Analogous to the lesson you were talking about in your
Speaker 2: own work, where getting the answer right figuring out if
Speaker 2: the person has sepsis is actually the only part of
Speaker 2: what you have to.
Speaker 3: Do huge and that's I think where AI as a
Speaker 3: field that a lot has a lot of growing up
Speaker 3: to do because historically the people who entered this field
Speaker 3: are you know, they gravitate towards the math, they gravitate
Speaker 3: towards the hired science. But what they don't realize is
Speaker 3: ultimately it is a people problem that you're solving. You
Speaker 3: have to get people to love it. You have to
Speaker 3: get people to incorporate it in their daily lives for
Speaker 3: this to be successful, and you have to operate in
Speaker 3: a world which is not very precise, like people have
Speaker 3: their faults and their mistakes and they work in a
Speaker 3: particular way, and you've got to get this thing to fit.
Speaker 2: Suchi Saria is a professor at Johns Hopkins and the
Speaker 2: founder and CEO of Asian Health. Today's show was produced
Speaker 2: by Edith Russlo and Gabriel Hunter Chang. It was edited
Speaker 2: by Karen Chakerji and engineered by Sarah Bruguer. You can
Speaker 2: email us at a problem at Pushkin dot Fm. I'm
Speaker 2: Jacob Goldstein and we'll be back next week with another
Speaker 2: episode of What's Your From