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We've learned from previous guests that machine learning and other forms of AI are helping to identify better disease treatments, get drugs to market faster, and spot health problems before they get out of hand. But what if they could also help patients find the best doctors for them, and help doctors frame their advice in a way that patients can relate to? This week, Harry's guest, Briana Brownell, talks about the computational tools her company Pure Strategy is building to find patterns in people’s personal preferences that can lower cultural barriers, enable better matchmaking between patients and doctors, predict which patients are most likely or least likely to go along with a treatment plan, or help doctors communicate their recommendations better. "Not everybody makes decisions in the same way," Brownell says. "Not everybody values the same things. But by understanding some of those psychological and value-based drivers, we can get better health care outcomes."

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Full Transcript

Harry Glorikian: Hello. I’m Harry Glorikian. Welcome to The Harry Glorikian Show, the interview podcast that explores how technology is changing everything we know about healthcare.

Artificial intelligence. Big data. Predictive analytics. In fields like these, breakthroughs are happening way faster than most people realize. 

If you want to be proactive about your own health and the health of your loved ones, you’ll need to learn everything you can about how medicine is changing and how you can take advantage of all the new options.

Explaining this approaching world is the mission of my new book, The Future You. And it’s also our theme here on the show, where we bring you conversations with the innovators, caregivers, and patient advocates who are transforming the healthcare system and working to push it in positive directions.

If you’re a regular listener you know I’ve had dozens of guests on the show who’ve explained how machine learning and other forms of AI are transforming healthcare 

They’ve talked about the ways AI can find better disease treatments, or help get drugs to market faster, or spot health problems before they get out of hand. In a way, that’s what the show is all about.

But my guest this week, Briana Brownell, thinks there are some gaps at the very core of our healthcare system where the power of AI is only beginning to be tapped.

And one of those gaps is the relationship between patients and their doctors.

Brownell is a data scientist and the founder and CEO of a consulting firm in Saskatoon, Saskatchewan, called Pure Strategy. 

The company works with all sorts of clients and industries. And it’s known for a package of computational tools called ANIE that uses forms of AI such as unsupervised learning and natural language processing to find patterns in data.

In the healthcare sector, Pure Strategy collects that data in the form of patients’ responses to behavioral surveys. 

And then it looks for patterns in people’s personal preferences or cultural identities that can help match them up with the best doctors for them.

These patterns can also predict which patients are most likely or least likely to go along with a treatment plan. That can help doctors communicate their recommendations better and raise the chances that patients will stay out of the ER or the ICU.

Brownell argues that medicine should never be completely data-driven, since doctors always need to account for patient’s unique life stories and preferences. 

But with AI, she says, providers can gather more input that helps them understand where patients are coming from and what challenges they’re facing.

All of which echoes one of the themes of The Future You, which includes several chapters about how technology is changing the relationship between us patients and our doctors.

By the way, the book is out now in paperback and ebook formats at Barnes & Noble and Amazon. So check it out.

And now here’s my full conversation with Briana Brownell.

Harry Glorikian: Briana, welcome to the show.

Briana Brownell: Thank you so much for having me.

Harry Glorikian: So, Briana, I've, like, read about what you've done. I've watched the TED talk you had given and seen you win awards and so forth. But I want to step back for everybody here and sort of, so they understand who you are or where you came from. And if you can give a sort of high level biography of yourself how you got to this point in your career, where you're building computational tools to help doctors and patients -- how did all of that start? Where did you grow up? What did you study? You know what? What are the experiences sort of shaped you to go in this direction? Because you didn't start off in health care.

Briana Brownell: That's true, yeah. I've had a really kind of a roundabout career, certainly. The first job that I got after my undergraduate degree in mathematics was in finance, which was wonderful. But I started in 2006, which I'm sure you know what's happening next. The global financial crisis happened next, right? And so that was my very first start in the work world. And after that, I actually got into more of the data science area, which was amazing for me because I was always interested in data, always interested in mathematics. But at the time, nobody had ever really heard of data science. Nobody had ever really been all that interested in analytics. And so I found that my job was so bizarre to just about everybody that I met. And so you can't imagine how excited I am when now data science is on everyone's mind. And, you know, artificial intelligence is, you know, a huge industry now. So I feel like, you know, I started somewhere very strange. But, you know, the world kind of came back to realize how interesting it really was.

Harry Glorikian: Yeah, it's interesting. I mean, when I was when I came up with the idea for my first book, it was, you know, at least five years before it published, maybe even six where it was like, Oh my god, . It's the data fixation of health care like. Once we get that data like, oh my God, we're going to be able to analyze it and then find opportunities and see patterns and longitudinal, and I was like, "But I don't hear anybody talking about that." So that's what I got me excited to write that first one. But tell us about your company. It's called Pure Strategy, which reminds me of Strategy Consulting, which was, you know, one of my last companies that I had. But you know, what do you you do for your clients? What do you sink your teeth into?

Briana Brownell: So, you know, first of all, the name Pure Strategy is a game theory reference. So I actually have a master's in economics. And so it's a little bit of a nerdy game theory reference. And so every time I meet someone else who took game theory, you know, we have a little bit of an eye-to-eye with the name of the company. But so the reason we named it that is a pure strategy gives you a way forward regardless of what your opposition does. So you always know the best thing to do next. And so, you know, with that philosophy is how we approach all kinds of different problems. So what kind of data, what kind of information do companies need to make decisions about how to better serve their customers, what markets to enter, how to invest their money properly? All of those kinds of things.

Harry Glorikian: I need to study pure strategy just to manage my wife and kids that so I know what to do every time something happens. But your core product at Pure Strategy is something you call automated neural intelligence engine or ANIE. What is Annie built to do?

Briana Brownell: So ANIE has a few different components to it. The reason that we built this intelligence system is because what I found was as a data scientist, a lot of the things that I was doing by hand could be much better done with an automated AI system. And so I began to look at the sort of time intensive but lower value tasks that could be tackled by artificial intelligence. And so we have a suite of four modules within that system that makes data analysis easier, faster, better. All of those good things. And so, you know, working with language, for example, working with prediction, working with choice modeling and then working to find emergent patterns and data that you didn't even know to look for.

Harry Glorikian: Ok, so NLP-based predictive capabilities. But step back for a second, so focus in a little bit on on, say, the clients in pharma and health care, because that's the constituency that generally listens to this. What kind of problems are you helping them solve? So if you had a few concrete examples.

Briana Brownell: Sure. So one of the areas that we find it's extremely useful is to understand typologies of patients and physicians and understanding how their values and attitudes impact their decision making. So not everybody makes decisions in the same way. Not everybody values the same things. But by understanding some of those psychological and value based drivers, we can get better health care outcomes. So we can look at what are the motivating factors in the patient group. Why are they being readmitted? Why are they not adhering to their treatment plan? Why are they doing things like delaying appointments, canceling appointments, those kinds of things? And then we can understand why they're making those decisions and hopefully sort of break the negative patterns and encourage the positive patterns so that they are healthier, they live longer, healthier lives and that their everyday life is improved as a result.

Harry Glorikian: Interesting. When you first started explaining it, my brain was going towards a dating app like making sure I put the right doctor and the right patient together.

Briana Brownell: So that's that's a big part of it, actually. Because certain physicians have a world view of their role as a health care provider, they need to be able to match their sort of delivery and their communication with a patient with the way that the patient can best understand it. So some physicians are very science-based and focusing on what are the cutting edge things that are happening in my field? And do I want to sort of use those with my patients to add to their treatment plan, for example. Whereas some other physicians are more looking at these sort of holistic care aspect where the patient is the center of a huge ecosystem of other health impact factors. And so how do they treat that patient as sort of an entire person? Right. And so definitely matching. You can imagine certain patients want certain kinds of doctors, right? So I'm the kind of person that I want to get in there and get out and give me the information. And that's fine, right? But that's not for everybody. And so by treating both the patient group and the physician group as having their own individual sort of beliefs and nuances within their worldview can really, really help things.

Harry Glorikian: So essentially, like, I'm simplifying dramatically, but we are talking about the fundamental functions of a sort of a dating app, at least for that application area.

Briana Brownell: That's right. Yes, it is a lot like a dating app. Yep.

Harry Glorikian: But so if I understood, because I was trying to listen to some of the things you had done and you've guys have written around it, basically you're trying to help lower the cultural barriers between patients and the medical system to make sure they get better care.

Briana Brownell: Yes, exactly. Yeah, that's a great way to put it.

Harry Glorikian: That sort of feels like a somewhat -- other than the dating aspect of it, right -- that feels like an unconventional problem for a computer science approach to tackle. I mean, we've had a lot of startup CEOs on the show talking about machine learning to sort genomes or chemical libraries, or to discover new drugs. But I don't think I've ever had anybody on, necessarily, that's trying to use AI to bridge a cultural gap. So I'd love to hear more abou that issue, like did you set out from day one to do this? I mean, you know, you've said in past interviews, it feels like you've been building a case that there are effective or emotional cultural issues at stake in the way doctors and patients communicate, and that if medical providers don't know about these issues or if they get them wrong, it can get in the way of achieving the best outcome for the patient. I mean, just summarizing. So if I'm wrong, you feel free to tell me,

Briana Brownell: No, that you know that that's a really interesting way of putting it. And so why did we realize that this was an important way to go? Well, part of the answer to that is because early in my career after the GFC [great financial crisis], before I started the company, I did a lot of work understanding the motivating factors in encouraging technology adoption for people who needed to mitigate climate risk. So that's a huge mouthful. But basically, we wanted to see what could encourage people to adapt to climate variability in farming and mining and wineries and grape production, that kind of thing. Because being able to understand how people perceive risk to their business, how people understand technology in terms of it being a business investment, how people sort of copy or don't copy other people in the community who seem like savvy business people in their own right, and then adopt because of the social factor. And so we have seen a huge amount of success using that methodology to understand technology adoption. And so it wasn't too far afield to say, OK, this same kind of technique that's so successful in this other area would have a huge impact in the health care area. If we could understand some of those value-based and behavioral elements to understand why people are making the decisions that they're making. Health care is such a deeply personal thing that you really can't treat it at that surface level, and that's really what we've been doing for generations. We've gotten so far away from that doctor and in the community who knows everyone and their family and who has that close connection. Now we've sort of taken a step back, tried to scale it up, but what we've lost is understanding how those core values impact the decisions that you make around your own health care.

Harry Glorikian: Yeah. Well, in the doctor's defense, it's sort of tough to do that in 10 minutes, right?

Briana Brownell: Absolutely, it is. And that's that's the problem, is, you know, maybe we can eliminate some of those pressures and bring that right.

Harry Glorikian: Yeah. And I and I look at sort of if I think about your system plus, you know, all the new technologies that are coming like wearables and so forth. So if you go to a doctor, they can get a longitudinal view of you, plus maybe the way that you're thinking about how you want your health care from the system that you're creating. But you mentioned you're solving these problems through machine learning or natural language processing. Why did you feel that these were the best tools in the AI toolbox to sort of help you with this?

Briana Brownell: So the typology creation is actually an unsupervised learning method. And so the reason that that's so effective is because it doesn't force a pattern on the data due to the bias of the researcher. So it finds emerging patterns that are in the data that someone might necessarily not know to look for that specific pattern. And so it's sort of it doesn't care about your or my preconceived notions about what kinds of attitudes and behaviors are important. All of that comes directly from the data. And so for me, that's a huge, really powerful reason that it's so effective. It's because it will find the patterns, even if it's not something you need to look for.

Harry Glorikian: So what's an example of the training dataset or the because I'm wondering like, you've got this system, but it's looking at certain sets of data. What would those be so that it can find those patterns?

Briana Brownell: Right. So usually it's a series of attitudinal and behavioral questions that the individual is sort of rating on, let's say, a seven point scale. And the way that we come up with that sort of battery of questions is a whole lot of conversations with the patient group. So usually you talk to a large number of folks and then patterns emerge using the natural language, understanding that you can then quantify in order to find the typologies. So we have partners to find patients and physicians in specific regions with specific conditions. All of that so that we can target people to get their sort of attitudes on these different areas.

Harry Glorikian: How do you distill all these squishy things like patient life stories, emotional states, cultural backgrounds, beliefs down into something that can be coded and categorized as data? I keep thinking about as spider graph, right? Yeah, yeah.

Briana Brownell: So so that's the hard part. You know, and I fully admit that it's a very challenging area because on the one hand, you have the sort of individual story that needs to be understood in context. And then in the other area, you need to have sort of quantitative data that you can actually make real decisions on. And so moving from that one part to the other is sort of a combination of experience of folks working with patients within that specific treatment area. It's a combination of the sort of cutting edge understanding of psychology, of how people interact with the health care system. There's a huge amount of cultural factors. We, you know, work with patients and physicians all around the world. And so that's always a huge sort of elephant in the room, to make sure to add context to it. And so by combining all of these things together, then you essentially get closer and closer to the right answer.

Harry Glorikian: So I'm almost thinking like, there's got to be this graphical interface, right, that somebody can look at quickly. I mean, I don't know why all of a sudden a Myers-Briggs popped into my head. So you get an idea of what that person is like and how to manage them. But so, I've heard you talk before, and you fundamentally believe, and you can correct me if I'm wrong, that it's the data plus the physician that takes it to a different level. It's not just the data itself.

Briana Brownell: Mm hmm. And I mean, it's the data and the physician in partnership with the patient because, you know, at the end of the day, we all have a role to play in our own health care maintenance, in our own sort of world through journey through this world, I guess, right? And I think that by empowering the physicians to, as we say, practice at the top of their license, that's really a positive thing for everyone, right? So instead of focusing on tasks that can and should be automated, you're really focusing on making sure that those outcomes are as good as they can be. And so the support system around the patient is also extremely important. So you had mentioned wearables and some of those things. So that is another area that we're involved in as well, is making sure that we have some of that data that can feed into understanding the world view of the patient. And then in turn, so the physician can understand where that patient is coming from and identify whether they may be having challenges with their maintenance, for example, or with something at home.

Harry Glorikian: Yeah, I mean, I've got my new book is coming out soon, and I, you know, by putting it together, I almost feel like the technology plus the physician can almost bring get the patient to have a concierge medicine level experience without the cost of concierge medicine, right? And so I'm assuming your system is trying to give them that elevated level of care by giving the physician the insights that they need. But does the patient also get the same insights to get to know themselves? I'm just curious.

Briana Brownell: They do, yes. And so we're actually looking at, rather than sort of -- you mentioned the concierge level medicine. We're actually looking at the most vulnerable people, rather than saying who needs the concierge service on the high end. We're saying, whose outcomes can we most impact? And so looking at the people who are more vulnerable, who struggle a lot more with their health care, where we want to make sure that we avoid them having to seek acute care. Because at the end of the day, nobody wants to end up in the emergency room, nobody wants to end up in the ICU. And so anything that we can do to sort of prevent that for those people is, you know, a huge positive for that individual and not only just them, but their whole support system, their family, their friends, everybody in their community.

[musical interlude]

Harry Glorikian: Let’s pause the conversation for a minute to talk about one small but important thing you can do, to help keep the podcast going. And that’s to make it easier for other listeners discover the show by leaving a rating and a review on Apple Podcasts.

All you have to do is open the Apple Podcasts app on your smartphone, search for The Harry Glorikian Show, and scroll down to the Ratings & Reviews section. Tap the stars to rate the show, and then tap the link that says Write a Review to leave your comments. It’ll only take a minute, but you’ll be doing us a huge favor.

And one more thing. If you like the interviews we do here on the show I know you’ll like my new book, The Future You: How Artificial Intelligence Can Help You Get Healthier, Stress Less, and Live Longer.

It’s a friendly and accessible tour of all the ways today’s information technologies are helping us diagnose diseases faster, treat them more precisely, and create personalized diet and exercise programs to prevent them in the first place.

The book is now available in Kindle format. Just go to Amazon and search for The Future You by Harry Glorikian.

And now, back to the show.

[musical interlude]

Harry Glorikian: So you grew up in Canada, you went to school in Canada. You operate a business in Canada. And so I'm picking on this sort of cross-border thing, right? Because our health care systems are just a little different. So, you know. But I also imagine you've worked with, you know, clients here in the U.S. and some based in Canada. I'd love to get your -- how do you think about the two systems when it comes to the implementation of a technology like yours? Because they feel like they come at health care from different vantage points.

Briana Brownell: Absolutely. So interestingly enough, we actually do work not just in the U.S., but also in Europe and also in Asia. And so for that reason, there's a lot of really interesting cross-cultural differences in how different health care systems work. And so, you know, Canada, we have a single payer system. And so for some, for some areas, it's a huge positive. People aren't going broke paying their medical bills. There's sort of more access in certain areas. But there are struggles. So things like remote communities, being able to have access to health care from places. For example, here in northern Saskatchewan, it's a real challenge for patients to get care from some of those remote areas. Each system, I think, has some challenges and some benefits. And then same with the American system, the advantage being that preventative care is actively incentivized, right? And so in Canada, that's not the case. So I think it's just really a different balance and a different tradeoff.

Harry Glorikian: So, so the system is designed -- I almost think you need to use the system to figure out your own clients so that you you can you can understand what their drivers are. But you've you've described yourself as a data scientist, a tech entrepreneur. But I've also heard the word futurist. So I'm super curious about, you know, let's talk about the future. So what do you think about the cutting edge ideas in AI? And, you know, do they really have the potential -- and I know what my bias is, so I don't have to cloud your thought with my bias -- but you know, whether it's in health care or business or other areas, what are you most excited about right now?

Briana Brownell: So for me, a lot of the interesting AI applications bring in decision making and sort of data analysis that is completely new and different. So if you look at things like diagnostics, the diagnostic tools using different styles of AI make their decisions in a way that's different than the physicians do. So you could have an AI system that's extremely accurate, but then it misses certain things that a physician will catch and then vice versa. And I think that that, to me is one of the most interesting and most important parts. Because now all of a sudden, you can have a sort of augmented system where the physician can work with the technology in order to get better outcomes for everyone. So that's one area where I'm really excited. The other area is being able to have that personalization at scale. So, you know, we talked about, you have the community physician that knew everyone's family and everyone's coming and going, and so you could have that personalized care. But then we've sort of moved towards a more kind of data-driven system where you didn't have that personal connection. I think we're going to go sort of back. I think we're going to be able to look at ways that we can personalize interactions, treatment plans, even specific medicines at a scale that it can really help a lot of people.

Harry Glorikian: Well, it's interesting because I was talking to someone at Facebook in their AI group and it was like, their system already knows so much about you, right? And so people don't realize like how that system truly does probably know them, better than they know themselves in a certain way. So I always think like, wow, if they could really start applying that to health care, you could really make a serious difference in the lives of these individuals, because most of health care is how you make your decisions and how you manage yourself. And did you take your meds? Did you go for that walk you were supposed to go for? Those sorts of simple things, right, that that we all struggle with on a daily basis. But so another futurism question. So you gave a TEDx talk in Calgary a couple of years ago where you talked about research done in pareidolia, just making sure I pronounce it correctly, which is the human brain's tendency to see faces and random things like in the environment, where you look in the I think you look in the clouds and you see a dog or something, right? But but you you tested computer vision. You found that that that doesn't happen. With the computer vision, they recognize different patterns, I guess, but not things like faces. And so from a philosophical question, how do you compare like the human mind and sort of the pattern recognition that we do? Because most of what we do in medicine is a certain form of pattern recognition. I'm just trying to figure out, is that what differentiates an intelligent system versus a conscious system?

Briana Brownell: So I would say in that case, it wouldn't be necessarily consciousness, but certainly the human brain works differently from the artificial intelligence systems that we've built so far. Most of the AI systems that we're building are sort of focused on one specific narrow task, and it does really well at one thing. But as soon as it moves outside of that, or as soon as you add sort of additional kinds of media to it, it's really, really challenging. So I think, you know, speaking futurism, the next wave of really good AI applications are going to widen. So we're really, really narrow right now. But we're going to start to widen more and more in order to sort of combine some of this information and be able to sort of get greater insights. So I'll give you an example. So when people do sort of codifying datasets for image recognition, what they do is they link it to what are called synsets. And so what a synset is is a meaning, right? So if you have, let's say, like a coffee mug, right? So you have a picture of this, you know, you say, OK, it's a mug, right? But then what if somebody else codes it as a cup? Well, so there are two different things, right? But they're similar enough that most humans would recognize.

Briana Brownell: Well, that's probably sort of really similar, right? But yet when we're doing image recognition and we're training on these huge data sets, that similarity is not always taken into account. So more and more we're able to make multiple linkages like that in order to improve the outcomes. But right now, in a lot of cases, that's not taken into account. And so that'll be I think the next step is, we're going to sort of widen some of the applications of artificial intelligence. And then after that, it's really about proactive and automated systems. So we right now are looking into this, being able to have a system that understands, adapts, and then makes a recommendation in order to improve health care outcomes. So this person is, let's say, their heart rate is constantly elevated. Maybe we need to send them a push notification and sort of ask them, Hey, how are you doing? Is everything OK? Right? Something like that. And so those proactive systems, I think, are going to become even more important in the next five or 10 years.

Harry Glorikian: So it's interesting. I was reading a paper yesterday or the day before about how there's, when you make, to speed up memory there's breakages that happen in the DNA in the neurons that sort of helps the system adapt more quickly to a new memory. And so. I want to say, like you're talking about systems that have to be able to change part of the code to be able to then adapt to what it's now looking at. So sort of learning, but not learning the way that we think about learning.

Briana Brownell: Yeah, so definitely, I mean, there's also challenges with those systems because you can have them quickly move away from where the original prediction was, right? And so being able to have that monitoring is extremely important. So this this is not a new idea. This is an old idea from the eighties about how you need to make like AI systems as collections of agents, right? So we're just digging up some of the old thought around this. But I think whereas it was extremely difficult to do 40 years ago, now it's actually relatively straightforward. And so I expect a lot of breakthroughs in that area.

Harry Glorikian: Well, and I think what you know, some of the other areas that I see is sort of where you turn AI on itself to figure out how to improve what it does, like Google's doing with new chipsets and so forth and so on. Which I think most people aren't factoring in -- the dramatic improvements that could be made when you turn these things on themselves. So the shifts are, what I like to call the turns, are happening much faster than most people anticipate. Let's go back to health care for a second. So try taking today's, you know, trends in AI, looking forward a couple of decades, say 2040. Shit, I'm going to be really old by then. But how do you think technology will change the way patients interact with the health care system, and maybe it's earlier than 2040, so don't let me. You know, that might be too far out, but what do you predict is going to happen at that point?

Briana Brownell: I think that there's going to be a much higher-touch system in place. So right now, most people go to the doctor for, maybe they'll go for an annual checkup, maybe not, depending on who you are. They'll go see a doctor when they have something go wrong, where they feel sick or they have an injury or that kind of thing. They might go to minor emergency if they had a sort of more serious injury or something happened there. But the truth is, it's not an everyday sort of a thing, or probably it's not an every week or every month kind of thing for most people. I see that changing. I think that there's going to be sort of a continuous back and forth. There's going to be a much more sort of low-friction way that anyone can communicate with a health care provider or even an AI system to get their health care questions answered. So, you know, I'm sure everybody has been in this situation where you either you feel sick or you have hurt yourself. There's something going on with your health care and you have to make a decision whether or not you're going to actually call and book that appointment and you're going to actually go down to the doctor's office and you're actually going to talk to some somebody about how you're feeling. I think that's going to disappear. I think it's going to be a lot of the sort of seemingly minor things are going to be sort of taken care of by high-touch technology system that can sort of direct people to a physician's care when they need to, but can handle sort of most other things that that happen. And so that drastically reduces sort of the load for things that are people are avoiding for months and months and months. And then all of a sudden it gets really bad and they end up in the emergency room. So I see that being completely eliminated from the system.

Harry Glorikian: Yeah. Well, that would be wonderful. It's funny because in my brain, I was going to, OK, the serious movie that lays all this out and it looks totally cool. And then the comedy where the person is totally revolting against the system. But I do agree, like, I truly believe that we're moving towards health care and hopefully away from sick care. Or we sort of push the sick stuff out much further. But like I mean, you can't see it, it's under my shirt, but I've got a CGM [continuous glucose monitor], right, that I'm wearing under my shirt here. And so, you know, why am I wearing a CGM? I'm not diabetic, but I'm sort of monitoring, you know, don't eat -- like, what was it we went to? I think I had bibimbap at a Korean restaurant, and man, whatever was in the rice made that blood sugar spike and totally stay up. So I'm like, OK, no bibimbap. Or if I do it, it's going to be once in a blue moon. But I think the systems are going to be monitoring. I don't think there's anything we buy anymore, your car, your computer or whatever doesn't have a monitoring system in it to sort of do preventive maintenance or alert you before, you know, here's the mean time between failure. And that's what I see happening and what we're doing.

Briana Brownell: Yeah, we even get, you know, I get my notification on screen time, like where I was spending time on my iPad, which app I was doing right. And so I feel like that's exactly where we're going to go to is where, you know, maybe every week you actually get a little sort of health care report or you get some some kind of information.

Harry Glorikian: Yeah, that the delivery of that information is going to have to be there. We're going to need a few geniuses on how to deliver that to people because I can just see a few people having fits, right? Because my kids don't like the monitoring app. When I say, how long have you been on Instagram or Snapchat? And they're like, Oh, not very long. And then you can see the time. And they don't like that. But do you believe, like every doctor or nurse physician assistant is going to have sort of an AI assistant working alongside them sifting through patient data? Highlighting what the doctor needs to focus on or translating cultural gaps? You're working on a system that sort of is trying to understand people, bridge that gap and sort of make things better, so I just see you're sort of at the beginning stage. And I'm trying to go forward in the future to say, would that just be the natural progression as it goes forward?

Briana Brownell: Yeah. So I definitely see multiple AI systems running behind the scenes that can sort of crunch the numbers and understand some of the macro level patterns that can then inform the physicians with information that might be relevant. So one of the areas that we've done some work in is with rare diseases. So you probably have heard the saying: If you hear hoof beats behind you, what do you think it is? Do you think it's a horse or a zebra? Right. So, you know, if you're a doctor and you see symptoms that match extremely rare disease, a zebra or something much more common, you're going to assume you're going to guess that it's a horse. But for the patient, you know, going through that rigmarole when you have a rare condition, when you are that zebra, that's a really difficult thing for the patient. And so if you can say, you know, this actually might be a zebra based on all of these other factors and all of these other sort of subtle cues, I think that that makes it better for everyone. I mean, for the physician who has access to pattern data that they would never be able to do by just sort of seeing patterns in their own patients and being able to look at that on just a much greater scale. And so that's an area where I think that there's going to be a huge, huge boon.

Harry Glorikian: Yeah, I mean, I'm a firm believer in genomic sequencing, to cut to the chase. And then, you know, I just interviewed Matthew Might, who looks at the genetic sequence and then helps identify already-approved drugs that might actually impact that disease state. You know, there's a number of things that are out there. I just wish they moved faster into the existing environment. And that's what drives me. I mean, I think at some point, I don't know how any of the systems can function without implementing these tools that sort of are assistive in nature. I've heard some venture guys say, "Oh, this is going to take the place of the doctor," and I'm like, "Oh my God, you're nuts." Like, that's not going to happen. But I think because I think every piece of data I've seen is the two together result in better outcomes rather than one or the other by themselves.

Briana Brownell: Yeah, absolutely. I think you're exactly right on that. The idea is that maybe you have a sort of larger system of people that can support people in their health care. So instead of focusing on doctors and nurses and then things like physiotherapists, et cetera, I see a role for sort of other support people within the health care system that can sort of guide patients to lead healthier lives. Aside from that, so if anything, I think that it's going to be we're going to need more people involved in doing some of these things.

Harry Glorikian: Yeah, I think, you know, I keep trying to encourage my brethren in the tech world to come to health care because it has more impact on on everything and we need more people. There's just not enough people to do the computational work or the real hard math, sometimes that's what is required. I find people being pretty lazy at that stuff that moves the needle. But it's been great talking to you. This is fascinating. I would, you know, I almost wish I could turn your system on myself to find out what my biases are. You know, you may want to come up with a consumer facing thing so that people can learn things about themselves and maybe even relay that back to their own physician about how they want to be communicated with.

Briana Brownell: Yeah, I love that. I think that right now we are actually working with a consumer facing application within the US system, so hopefully someday you'll be able to have access to it and you can learn all about yourself.

Harry Glorikian: Yeah, like I said, I mean, I'm simplifying it, but sort of like a Myers-Briggs. When I was younger, I was ENTJ and now ENTP. But, you know, always good to know yourself. Great to speak to you. I wish you incredible success in your endeavors. And we want to see systems like this making impact on patients and bringing hard data to the table to get even the system itself to sort of change the way that it operates.

Briana Brownell: Wonderful, well, it was great to talk to you, and, you know, it's always something that I am excited to chat about, so thank you for having me.

Harry Glorikian: Thank you.

Harry Glorikian: That’s it for this week’s episode. 

You can find past episodes of The Harry Glorikian Show and MoneyBall Medicine at my website, glorikian.com, under the tab Podcasts.

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Thanks for listening, stay healthy, and be sure to tune in two weeks from now for our next interview.