Revolutionizing Medical Tech with AI | Hamed Shahbazi

3 Sep 2024 · 46 min

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In short

Podcast Notes: The James Altucher Show - Revolutionizing Medical Tech with AI | Hamed Shahbazi

Episode Overview

  • Host: James Altucher
  • Guest: Hamed Shahbazi, CEO of WELL Health Technology and Chairman of Healwell AI
  • Main Topic: The transformative role of AI in healthcare, focusing on current advancements and future implications in patient care and medical technology.

Key Themes and Insights

The Current Impact of AI in Healthcare

  • AI is not a distant future concept; it is actively reshaping healthcare by:
  • Predicting Diseases: Identifying rare diseases using complex data analysis.
  • Personalizing Treatments: Analyzing individual patient data against vast datasets to recommend tailored health advice.
  • Enhancing Healthcare Efficiency: Automating routine tasks (like data entry) to free up healthcare providers to focus on patient care.

Concept of Healthcare as a Service (HaaS)

  • Hamed discusses the emerging model of Healthcare as a Service, which:
  • Provides continuous health monitoring via AI-driven platforms.
  • Encourages proactive rather than reactive care.
  • Streamlines patient-provider interactions for better accessibility and efficiency.

Discussion Points

AI's Role in Disease Diagnosis

  • AI's capability to analyze data allows it to identify diseases that might be overlooked by physicians.
  • Case studies show AI successfully diagnosing rare diseases with precision.

Real-Life Applications of AI in Healthcare

  • Examples of AI diagnosing chronic conditions and recommending appropriate interventions.
  • Insights into how AI can track patient health trends over time, enhancing preventive care.

Importance of Data in AI and Healthcare

  • The significance of data collection in improving healthcare outcomes.
  • Hamed emphasizes the need for structured data to teach AI effectively to identify health risks.

Future of AI in Healthcare

  • Predictions of AI's role in transforming the patient experience in healthcare.
  • Introduction of AI tools that can assist physicians during consultations, potentially improving diagnostic accuracy in real-time.

Key Takeaways

  • AI is Revolutionizing Healthcare: From predicting illnesses to personalizing treatment plans, AI's influence is reshaping how care is delivered.
  • Enhanced Patient Outcomes: With AI, healthcare becomes more proactive, leading to better patient outcomes and streamlined processes.
  • Value of Continuous Monitoring: The future of healthcare may hinge on continuous data tracking and AI's analytical capabilities, offering insights that can preemptively address health issues.
  • Healthcare as a Service: The shift towards a service-based model reflects a growing trend towards personalized, accessible care using advanced technology.

Chapters

  • 00:00 - Introduction and the Evolution of Healthcare
  • 03:10 - The Oura Ring and Continuous Health Monitoring
  • 12:15 - The Role of AI in Health Data Analysis
  • 19:35 - Future of AI in Personalized Healthcare
  • 26:00 - Real-Life Examples of AI Diagnosing Rare Diseases
  • 35:20 - Challenges and Benefits of AI in Medical Diagnosis
  • 42:40 - Concept of Healthcare as a Service
  • 46:55 - Future Predictions and Parting Thoughts

Resources

  • [Oura Ring Official Website](https://ouraring.com/)
  • [Well Health Technologies](https://wellhealth.ca/)
  • [Hamed Shahbazi on LinkedIn](https://www.linkedin.com/in/hamedshahbazi/)

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This episode is a deep dive into the potential of AI in healthcare, shedding light on practical applications and insights from an industry leader. For those interested in the future of medical technology, this episode provides valuable perspectives on the ongoing transformations within the healthcare landscape.

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Transcript

Automatic transcript. May contain errors.

0:00Look, as a manager of people, as an employer, and as an entrepreneur, and even an investor in startups, I can tell you the most important important thing is the quality of all the quality of people. you hire. The best part is that great candidates are already on LinkedIn. Employees hired through LinkedIn are 30 % more likely to stick around for at least a year compared to those hired through the leading competitor. And I will tell you that the great thing about LinkedIn is that you're not just looking at random people. You're able to see the people who your friends and trusted peers and colleagues who they trust and who they've hired in the past and who they recommend.

0:42And hiring doesn't have to be complicated. Realistically, when you have a business to run, you don't want to spend hours on hiring. You want to hire the right person as quickly as possible. That's why LinkedIn Jobs AI Assistant suggests immediately 25 great big candidates daily so you can invite them to apply and keep things moving. Hire right the first time. Post your job for free at linkedin.com slash altature. then promote it to use LinkedIn Jobs' new AI assistant, making it easier and faster to find the top candidates. That's linkedin.com slash Altucher. Post your job for free. Terms and conditions apply.

1:19This isn't your average business podcast, and he's not your average host. This is the James Altucher Show.

1:37Okay, so we're talking about the Oura ring. Spell that as an O-U-R-A. Yeah. Okay. Yeah. And you're wearing one. Yeah. So basically based on, it's collecting things like heart rate variability, temperature, resting heart rate, and even minute amount of perspiration around that. It can sort of detect all these elements. And so based on all these things, it starts to infer, it has a bit of an algorithm where it determines whether or not you're more stressed or less stressed. I guess when we are stressed, things happen to our heart rate, things happen to the temperature even of our skin, you know, it can sort of go up and down.

2:10And then they've been able to basically, you know, push that into some kind of algorithm that then punches out some kind of stress response. And have you noticed that since you got the Oura Ring, because now you measure your stress levels every day, have you become less stressed? Yeah, it's really interesting. I do think it's helped because it does. I go back and I look at my day and I'm like, okay, that's interesting. I didn't really, I didn't really notice that I was stressed there, but was I, you know, what, what was I doing? You know, and it, it does sort of train you a little bit, particularly with sleep.

2:43So when you're asleep, you, you know, you're not aware of all the things that are happening to you. You're not aware of all the things that you do before sleep that impact your sleep. Right. And, and, you know, if you had drinks, if you had a late meal, if you slept late, you know, all these things impact the quality of your sleep. And so So it's really helped train me because I want better quality sleep because it's very important for health, ultimately, you know, having that rest and restoration. So I would say, yeah, it's just sort of trains you and having an active guide as to what happened during the day, the things that you did, your behaviors during the day that contributed to your stress response and all these different vitals, you know, you know, it has this kind of cumulative effect that that kind of helps you act better over time.

3:27I've been afraid to get one of these devices that track everything because I'm afraid I'll just get obsessive about how many steps did I walk? How much hours of quality sleep did I get? Is my heart okay? Am I too stressed? I'm afraid I'll just become like overly, the way people check their email all the time, I'm afraid I'll just check this all the time and get obsessed with the data. But maybe it's useful. You know what? I think you're right, though. I do think about this sometimes. I used to check my email when I woke up. Now I check my sleep score. I am pretty obsessed about it. But what I've talked to people who have had it for a long time, and they say, look, it's good to have for at least a couple of years.

4:12And then you've kind of gone through that training I just spoke about earlier. And then once you've done that, you don't necessarily need it as much. So then you can sort of put it away if you think you've gotten a bit too obsessed over it and you're tracking things a bit too closely. Because really the benefit is in that training. Once you've had the training, you know what happens when you eat late, when you drink, you know how your body works. So now you don't necessarily need all the detailed analytics every day. All right. I'm going to get one. And the interesting thing is, and this is going to drive us to one of the topics I really want to talk to you about, is the data aura the company must be accumulating is probably amazing for in terms of training AI.

4:55So, so probably really figuring out the, the, the roots of stress and heart attacks and strokes and, and, you know, maybe other things like, oh, if among the million peoples who use our ring, the people who went down in income this year only got six hours of sleep, as opposed to the people who went up and income got nine hours of sleep on average. So there's going to be all sorts of conclusions and data that the AI is going to determine from this. It's unreal. You're absolutely right. Actually, we were just talking about this earlier, but Aura's got so much data. I heard the CEO talk about this at a conference years ago.

5:34He was saying that they may be one of the best predictors of whether or not someone has COVID just based on the signature of all these things that I just mentioned, heart rate variability, temperature uh you know resting heart rate and your sleep patterns and yeah they it it tells a story right and and so now what they do they is they also are able to tell you your um your cardiology age like your heart health age how how old is your heart compared to how old are you um that's interesting the other thing that it does is it it it's now starting to tell you if it thinks you're going to get sick based on some of the AI response, um, uh, in terms of, of being able to learn based on your data and other people's data, what, you know, and, and what happens to your vitals right before you get sick.

6:24Um, cause it has, cause it has a million people who have gotten sick at different points. And if you start showing characteristics like, oh, you just went down in steps per day. Your sleep went down. Um, your heart shows you probably are on a plane traveling because maybe that your heart changes there. So, so you're going to get more likely to be exposed to some, some bad air. Who knows? Well, let me ask you, can you, can you buy the data? Can you license the data? I don't know. It's a really good, it's a really good question. I think you're right though. That's probably their, their biggest, you know, you know, kind of asset as a company is that they've accumulated all this, you know, sort of first party data, right?

7:05It's their device. They've collected all of this data and they're likely looking for patterns and anomaly detection amongst all of it. So it's fascinating. I don't know if they'll ever open source it, but I know this is an area, you know, this kind of activity tracking and biometric tracking that's really, really on the rise. You know, the whole idea of our lives medically is that, you know, we used to be checked out once in a while. once in a while we'd get a checkup once in a while we'd get our vitals checked and now we're getting into a world where we're going to be continuously monitored and and and that continuous monitoring that data becomes really valuable because you know people have said hey you know my apple watch saved my life because it was able to predict that i have arrhythmia and and and and i was you know you know so we're getting into that into that stage of of life now where these things are starting to get, you know, quite, quite smart and quite continuous in sort of their, their, their evolution in terms of focusing on our data.

8:09Yeah. And, and, and using the AI or even very sophisticated statistics to kind of make, you know, and it's not like, we're looking at this as, well, okay, it's collecting healthcare data so we can make healthcare predictions, but maybe we can predict other things like, oh, the people with these health characteristics, vote Democrat. People with these characteristics are more likely to make a buy when they see an ad because they didn't get much sleep last night. And here's an ad for clothes. And somehow people who get sleep who have these other characteristics need clothes. Totally. I think those data relationships could be really interesting.

8:50I saw an ex the other day, someone had built an ETF of stocks based on CEOs that lift weights. Like they're avidly into wellness. They lift weights. And they're really dedicated to it. And it was like striking how much better than the S &P they'd performed as a group. That's interesting, right? I believe it. you know about a year ago i was i was so i was competing in um one of the various u.s senior championships for chess and somebody pointed out to me that the winners of there's like three or four tournaments that kind of are the championship for for older people in the u.s for for chess someone pointed out to me that all the winners that year you could tell lifted weights like they had you know even though it's chess is a game where you're just sitting down you don't you wouldn't think it's related to lifting weights.

9:44It does give you more stamina. It gives you more focus. And there's something to building muscles that is healthy. Absolutely. Our minds and our bodies are connected. And if we have a stronger body, I think we would have stronger minds. And also what happens is when you lift weights, you have better glucose control. We now know that glucose has a lot of insidious effects, not only in terms of our body, but also our brain. We're starting to think that dementia and neurodegenerative diseases are a form of type 3 diabetes. We started to hear that a little bit. So lifting weights is a great way of glucose control because our muscles soak up more of that glucose.

10:36So yeah, the moral of the story is you got to lift, especially as a man, as you get older, if you don't use it, you lose it. You start to get sarcopenia, then you've got other risks, you fall down and early death, all this stuff, right? I got to start lifting weights. The weights are sitting right there. I'm looking at weights, but I just never touch them. So Hamed Shibazi, I want to talk about perhaps the most important topic of our lifetime, which is the role of AI in healthcare and how healthcare is going to change, not 15 years from now, but like in the next few months or year or two years, three years, like the whole world is going to be unrecognizable.

11:19And like people talk about AI with robots and self-driving. I don't care about that at all, but healthcare is going to 100 % change. You're the founder of Well Health, co-founder of Heal Well, which is spinned out of Well Health. These are both public companies, but HealWell does a fascinating, fascinating thing with AI and healthcare, which we'll talk about. But first off, you started out as an entrepreneur, classic story from nothing. Sold your first company, I think it was your first company, for$340 million to PayPal. What's the story? We're going to talk about healthcare and AI, but I just really want to know, how'd you become an entrepreneur?

11:56Thanks for asking. And it's just, I'm delighted to be with you. Love your show. your broadcast it's great to be here um look i i i love solving problems as an entrepreneur um you know i was always just curious about solving problems i i i kind of consider myself to be a tech generalist so the first place where i was applying technology to help improve processes and you know different businesses was it was in fintech where we were trying to speed up payments lower the latency of having them post to the back offices of different billers like utility and wireless billers. This was important because a lot of people were paying a lot of money for last minute bill payments, these emergency bill payments.

12:40And we sped that up and we started to save consumers hundreds of millions of dollars because we were able to do that. I don't understand. So what was it doing? So I'm a consumer. So let's say you need to pay your utility bill or your car bill. And if you don't pay it today, you're literally going to get shut off. Like, like someone's you've, you've kind of blown through your due date and whatnot. And, and, and you would, you would typically go to like a Western union or a money gram, and they would do like a money transfer for your bill payment. And they would charge you like eight, 10, 12 bucks for, to do that.

13:15Um, and, and we came along and we said, that's, that's egregious. That's crazy. You know, we should be able to competently post a payment to someone's back office, um, you know, for a lot less than that. and we established links into the back office of lots of different billing companies. So how would I make my, so let's say I'm past my grace period, utility company has sent me five notices. I'm about to get my electricity shut off. A, how do I hear about your company as opposed to just going to Western Union like I always do? And B, what do I do? We actually became an option for a lot of the billers.

13:53You could go to the biller site directly. You could go to the convenience store. We actually had self-service automated kiosks spread around where you could actually put cash into those kiosks. And you could then, if you were under banked, or let's say you were cash preferred, you know, there was a bunch of these different ways that we could acquire the transaction, mainly outside of the bank channel. So that was the thing that we were focused on is the low to moderate income demographic that was paying outside of the bank channel.

14:24Take a quick break. If you like this episode, I'd really, really appreciate it. It means so much to me. Please share it with your friends and subscribe to the podcast. Email me at Alcatra at gmail.com and tell me why you subscribed. Thanks.

14:45Look, as a manager of people, as an employer, as an entrepreneur, and as even an investor in startups, I can tell you the most important thing for your business is the quality of the people you hire. The best part is that great candidates are already on LinkedIn. Employees hired through LinkedIn are 30 % more likely to stick around for at least a year compared to those hired through the leading competitor. And I will tell you that the great thing about LinkedIn is that you're not just looking at random people. You're able to see the people who your friends and trusted peers and colleagues, who they trust and who they've hired in the past and who they recommend.

15:28And hiring doesn't have to be complicated. Realistically, when you have a business to run, you don't want to spend hours on hiring. You want to hire the right person as quickly as possible. That's why LinkedIn Jobs AI Assistant suggests immediately 25 great fit candidates daily so you can invite them to apply and keep things moving. Hire right the first time. Post your job for free at linkedin.com slash altature, then promote it to use LinkedIn Jobs' new AI assistant, making it easier and faster to find the top candidates. That's linkedin.com slash altature. Post your job for free. Terms and conditions apply.

16:04This message comes from Capital One. With the Spark Cash Plus card from Capital One, you earn unlimited 2 % cash back on every purchase and get big purchasing power so your business can spend more and earn more. Steven, Brandon, and Bruno, the business owners of SandCloud, reinvested their 2 % cash back to help build the company's retail presence. Capital One, what's in your wallet? Find out more at CapitalOne.com slash Spark Cash Plus Term Supply. so in 2003 one of the first articles i ever wrote about stocks and finance i called it in retrospect the name maybe is not so good but i called it the poverty index and i i recommended because there was a recession it was 2002 there was a bear market as recession was happening and uh i recommended like dollar stores rent-a-furniture stores uh pawn shops and um payday lender stocks and those stocks actually did do very well and they were they were they were good businesses because like you say the the underbanked when when you're banked you don't understand that there are actually a large percentage of the population that don't even have bank accounts or or bank accounts they can regularly use because they're expensive there's all these fees so so services for the underbanked do do very well um and and And because they're local and community-based, it's surprising how few people default on, like, let's say, rent-a-furniture situations or payday loans and so on.

17:42Yeah, absolutely. And, you know, it's expensive to be poor, unfortunately, right? And so it's sort of counterintuitive. You know, I shouldn't have access to cheaper services because I don't have as much money. But no, I actually have to pay more just to expedite a payment. And so this issue of accessibility and technology is a really big thing. And this is what we've tried to do at Well Health is to try to give that higher end experience to just normal primary care visitors. that allow them not to have to come in and sit for a couple hours for their doctor to show up, to be able to respect their time through digital technologies, allow them to book online, allow them to feed information into the clinic to save them time, allow them to check in on their mobile phone.

18:38So we've developed all this digital patient engagement to help patients and clinics interact with each other. and then we've applied that to an enormous owned and operated network. So we're the largest owner operator of outpatient medical clinics in Canada and we own a significant number in the U.S. as well. So overall, Wells have about a billion dollars in revenue this year, very profitable, 130 million EBITDA is likely what we'll get to generating pretty significant cash flow. And And we're doing millions of patient visits. And our worldview is tech-enabled healthcare. It's incredibly important to us.

19:20We're not just out there just trying to be an owner-operator. We really want to be as tech-enabled as possible so we respect people's time and deliver a better experience. And I want to talk about Well Health, your clinics, but also Heal Well, which is you also, as we were talking about with the Oura Ring, You have millions and millions of patient records that you've been able to create an AI model on. And I want to talk about how this is going to literally change the future. But I still am fascinated. How did you start your first company? Like, what was the transformation that took you from, you know, whatever to entrepreneur to successful entrepreneur?

20:06Thank you. Well, I think, you know, I think it was just, I think as an entrepreneur, you have to be, have a bit of audacity to think you can make a difference. But what was happening in your life? Like, what were you doing? You know, it's interesting. This was really right out of school for me. I had just finished my engineering degree and I did engineering because, you know, growing up in a Persian family, I was sort of given the choice of being a doctor or being an engineer. And my dad was an engineer. So once I finished engineering, I loved it. It was very interesting. But I just really realized that I don't want to be an engineer.

20:48I realized that I want to be a businessman. And so it was around the time when, you know, we started to see really the rise of, you know, the graphical Internet. And I just knew that I really wanted to be part of that of that growth. I wanted to be involved in and this is before iPhones. You know, I'm not I'm not in my 20s or 30s anymore. Right. So and I could just see that the Internet was going to disrupt everything. And so, you know, it became a really, really big priority for me to get involved in this technology revolution and apply, you know, the digitization and modernization theme to different businesses.

21:32And that's really what I've done all my life now. I've applied it to fintech. I'm now applying it to healthcare. It was remarkable that I even had the chance to do that with healthcare. It just goes to show you how much lag there is in adoption of digital technologies. Particularly with healthcare. Like healthcare, I feel is 30 years behind the times. A hundred percent. And so, you know, in 2017, 2018, you know, around the time that I'd sold my previous business, I remember having these experiences in doctor's offices and I was thinking, this is unreal. Like I could have had the same experience 10, 20, 30 years ago.

22:08The doctor came in, was handed his, you know, paper folder and he wrote paper notes and And I got a paper, you know, kind of referral that I had to then use to call someone else with the phone. And it's just like everything about it was not digital. It took forever for me to get in. There were people running around everywhere in the clinic. It didn't look very organized. And I was like, you know, I wonder if this is like an anomalous situation or is this the state of play in healthcare? And so I started to do some research and I was like, I was just floored. And at that time in Canada, we had 0.25 % penetration in telehealth throughout the country, which is unreal.

22:49It's really, really low at a time when... How do you define telehealth? Telehealth is a patient physician consultation that occurs in any other format rather than an in-person consultation. So it could be over the phone. It could be an audiovisual kind of link like this. And, you know, 16, 17, we've got iPhones everywhere, all over the world. And so the idea that there wouldn't be, you know, video calls are commonplace, but they had not really made their way into healthcare. Some of the different HMOs were doing this, like Kaiser Permanente in the US had had already quite a bit of growth in telehealth and whatnot.

23:34But as a country here in Canada, we were quite low. And just for contrast purposes, two years later during the pandemic, because of physical distancing, we got to a high of 80 % penetration of telehealth. So 80 % of all visits were attributable to telehealth and 20 % will not. And now it's now post-pandemic, we're back down, but we're not back down to the same levels we were before. Pretty much in all industrialized countries, we're in that 40 % to 50 % range. So forever, this industry has now changed as a result of the pandemic. You can't say that about too many industries where forever they're changed.

24:11Now there's an entrenched kind of behavior and preference to see physicians online for certain types of appointments. That could be mental health. It could be chronic disease check-ins. It could be for prescription renewals, all that kind of stuff. Yeah. And it seems like, well, first off, it seems like the general trend for humanity is we don't ever want to leave our house. like you know right now i can get a chef to cook me food and somebody else will then pick up that food deliver it to me and then i can watch a newly released movie streaming right into my house like i just never have to go anywhere and i can have a telehealth physician to you know give me a prescription for something and i can go out and get antidepressants and testosterone and whatever just deliver to me.

25:02All delivered perfectly. Yeah. Yeah. And if you want someone to actually give you the shot, there are actually people now, there's all kinds of businesses across the country. They'll come over and they'll give you that B12 shot or they'll give you, and they'll administer the shot for you if you don't want to do it yourself. Yeah. I just applied for life insurance. So someone came here and took my blood and did all the blood tests. And then also I used used the, I haven't used this lately, but the IV doctor where they come over and give you like vitamin C and whatever. You know, I would do that before traveling, but also with telehealth now, you have more opportunities to collect digital records, right?

25:42So like what sorts of data did you, once you were doing, having millions of patients do telehealth with your clinics, what sorts of data did you start to collect? Yeah. So I'm really glad you brought that up because thematically speaking, electronic medical records have been around for a while. And while there has been less usage in the past, that's growing tremendously. And for a long time, doctors have been entering in all this data, right? Entering in their observations of the patient, what they're dealing with, what the assessment of the visit is, what the care plan is, what drugs they're taking, whether or not they're working, all this kind of stuff.

26:23All this information is going into the patient record. And, you know, if you're seeing a specialist or you're getting labs done on your blood, all that stuff is coming into the primary care record. And the problem is all this information has been going in and very little value has been coming out. And this is where our partnership with HealWell, our spin out of our data into HealWell has been tremendous because we now have, you know, physician co-pilots, like AI co-pilots. What is AI really good at? Taking a vast amount of information, very complex, very complicated, and being able to actually create very simple insights and be able to deliver those to people at the right time and generate those insights based on that kind of anomaly detection and learning from data.

27:11What's an example? There may be a certain pattern, a certain signature to a certain disease. Let's say how your blood tests come back, the types of vitals that you exhibit in terms of your enzyme levels or whether or not you're prone to diabetes or your BMI. All this kind of stuff could inform the view that maybe you have some kind of diabetes or chronic kidney disease or anything like that. Maybe the doctor hasn't picked that up because they have not been able to review all the information. there's so much information in the patient record it's really hard for the doctor to go in and connect the dots and integrate the information on the fly especially when you consider that here in Canada doctors are getting paid 30 40 bucks for primary care visit in the U.S.

28:01it's not that much more right so it's a lot of work to do for a little for a little bit of money and so um so so basically this software will will you know what we call risk stratify the patient for particular diseases. It'll say, hey, these are the patients that are red, yellow, green. They're at risk for heart disease. They're at risk for diabetes. They're at risk for chronic kidney disease. Or they're red, meaning like... I'm sorry I keep interrupting. I know I'm going to get complaints later from listeners that, oh, you're going to interrupt too much. But I just want to, I get curious about, is the problem that...

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28:41I feel like when I get a blood test, and I've barely in my life. I haven't been to a doctor's visit since I was like 18 years old. So, and I get a lot of criticism for this from my wife in particular, but is the problem because when you get a blood test, okay, here's my liver counts. Here's my enzyme counts. Here's this thing, that thing. And the doctors just look at one thing at a time and say, oh, these high liver counts mean liver disease. Is the problem that they're just looking at one, you know, one thing means this one another thing means this and they're not looking at all of the how the different data pieces of data interact with each other and maybe a slightly elevated count in this area is related to a slightly elevated count in this area could mean some cancer or some other horrific disease that you have but but most doctors don't know all these correlated relationships but ai does you're absolutely right on exactly so so like what's an example of that though and i'm sorry I'm just trying to understand.

29:42Yeah, yeah. I mean, I think it's a lot of those, I think part of it is observational things that the doctor will write over time. And so there's a longitudinal element here. There's a timeline element here. So the AI will review all the different clinical notes. And this is what AI is really good at. It's actually taking unstructured information and making it structured. So imagine all these various notes that are just sitting in the record over time. And it'd be hard to go in and read all of them. They could be thousands of pages of PDF, but a machine can do that fairly quickly, right? And so it can look at those doctor's observations over time, what your BMI was, what these results were like, and it can piece together a story.

30:33It's like, oh, look, it's not just what happened at this blood test. It's what happened to all those blood tests that occurred over the last several years and all the different observations the physician had. Based on this, it could mean that you have a rare disease. And rare diseases are interesting because there are literally thousands of rare diseases and ultra rare diseases. This is a$200 billion market for pharmaceutical companies. A lot of those rare diseases have pharmaceutical interventions. And some, like in Canada here, there's some rare diseases where you'll have three or four customers, you know, patients that become customers for particular pharmaceutical interventions.

31:15But it's really hard to actually identify whether or not someone is subject or has exposure to some kind of rare disease. So what's a story slash disease where prior to AI, nobody would have or would have been very difficult to figure this out? after AI, this is a specific example. Like you have a story of a patient where they had symptoms over years, but you weren't getting diagnosed correctly. And then suddenly AI and boom, they're diagnosed. Yeah. I mean, we have tons of those. I mean, I probably couldn't rattle off specific ones, but I've heard tons of these types of stories. And we all know someone who hasn't been feeling well for a while and no one can quite, you know, put the mark on what's going on with them.

32:05And, you know, they're like, I've been, I've had so many tests and, and, and, and, and, and, and I've talked to so many people and no one's been able to figure it out. And I've gone from person to person to person. And it's because a lot of times it is some form of rare or ultra rare disease. And, and, you know, we now, you know, support hundreds of them through the heal well technology, there are hundreds of these diseases that, that, that our co-pilot, you know, uh, supports and can identify. Um, and, and, and, and that's, that's extremely valuable for a physician, but the most common diseases are chronic care diseases.

32:42So, so we we've, we've seen this a lot. We've seen, we've seen people that are, that are, that are degrading and, and, you know, good things are not happening. Uh, uh, even, even if they're identified for a particular disease, like let's call it chronic kidney disease. If they're under dosed in terms of the pharmaceutical interventions that they're taking, we've had a particular case I was looking at the other day. This particular patient was not doing well. And the co-pilot identified that they're not taking the right dosage. So it's just something simple as that changed this patient's life forever because now they're taking the right dosage and they started to improve right away.

33:25What did the doctor say? Oh, if you're not feeling well, but we think we got the diagnosis right, it's got to be the dosage. Why did the AI have an advantage over the doctor? Because it was able to kind of look at the data and recommend a particular dosage. And for the particular progression that that disease had with that person, the dosage should have been much, much higher. And, you know, these pharmaceutical interventions are sometimes just useless if you don't have the right dosage. Like you might as well not even be taking it, right? And so dosage and adherence are incredibly important in terms of, you know, beating back whatever ailment that you have.

34:19So, okay, how did you train the AI? So you have millions and millions of patient records starting from, let's say, the initial phone call. I've got a stomach ache. I've got a headache. Then you have a doctor's visit. A doctor takes all the notes. Then you have the next visit, the next visit, the next visit. You have all these millions of records. Just tell me technically, how did you train the AI? Like, what did you do? It's not easy. So because of the complexity of the human body, you have to go disease by disease and you have to go, you have to do a really deep dive. You have to kind of bring on specialists and experts in that particular field.

34:56You need to look at tons and tons of data. You need to identify essentially what the signature of that disease is. Like it, you know, like it is characterized by, you know, certain types of labs, certain types of progression in terms of the degradation of certain, you know, vitals that could be showing up. So some of that could be rules-based, like it doesn't have to be AI. But what's interesting about AI is just the ability to take unstructured information and structure it. That's really hard to do because most of the information in the patient record isn't just perfectly laid out in tables. That's what we call structured information.

35:35You can run a search, easily comes back. But the vast majority of information is in there, it's unstructured. So someone, an AI has to read that, whatever was written there and try to interpret what did it actually mean? What was intended by the physician that wrote this in context with everything else? That's where it gets really exciting because then it structures it and then it can do anomaly detection. It can say, oh, based on all this information, now we know that we are actually pretty close to this particular disease. And this is what rules-based doesn't do because rules-based requires you to perfectly hit on those criteria.

36:12If there's 10 variables, there's like 10 to the 10th number of possible variations of those variables and conclusions. So obviously this AI was trained on data. So you have millions of records where you have years of patient record, millions of patients where you have years of records. And at the end of the day, there is a diagnosis that the doctors agree on. You have this training data. How did you then feed it into an AI? How did you build the AI? You know, it's really... So what the AI is doing here is basically processing all of this data and learning. So a lot of times you're basically writing algorithms that allow the AI to just review the data, process it, learn from it, and identify the anomalies.

37:11And as you find the anomalies, you start to build software around these anomalies mean certain things. And a lot of times, machine learning is essentially trying to find out something based on this data that you don't know about. A rules-based filter, you know, you're looking for something, you say, if this and this and this are true, tell me what the output is. In machine learning, you don't know, you say, tell me what the needle in the haystack is. Find me the needle in the haystack. That's what machine learning is good at. And when you do that enough times, and this is why you need so much data.

37:47If you do that enough times, you start to correlate relationships. You start to say, oh, we found the needle in the haystack this many times in this data. It means that we have now identified a real pattern. And it's that pattern detection that AI is so good at. Does that make any sense? Yeah. And so that's where the kind of supervised learning burst takes place, right? So you had all these patients with all these diagnoses, they're in a big system. And then this system sort of figures out all the possible contexts that are related to each disease. And now given a new patient record, it then basically spits back, oh, and you don't even know which patients it's looked at, which patients it's correlated to, but it figures it out and it spits back, okay, this person might be at risk for this rare disease.

38:39Right, yeah. And the process that I was talking about, and you touched on too, is about training the data. So once you train the AI, so the AI starts to understand certain things, then it becomes a lot more useful in being able to respond to queries and make that data a lot more useful. And then there's basically two forms of AI. There's symbolic AI, and then there's generative AI. You know, what's interesting about generative AI is that it's literally predicting what the answer is going to be based on this enormous amount of data. And so one of the things that we've done with one of our co-pilots is actually having the co-pilot, the service actually listen to a patient and provider conversation.

39:26And taking all of that conversation and generating a medically relevant note. This has been a huge game changer for physicians because typically physicians will have to take a transcript of the entire meeting, of the entire consultation, and they'll have to actually make that note. That note has regulatory relevance. Typically, that's how you get paid as a physician. You need to make that note into the record. And that note has a particular structure. It can't be a super long note. It has to be fairly short. And it sort of talks about the situation, what the assessment is, what the care plan is moving forward.

40:00And so doctors spend a good part of their day just generating this note, right? And this is why they're taking all these notes and transcripts while you're speaking. We found that that's returning 20 % to 30 % of a physician's day back to them just by generating this note. So that's pretty incredible. So we're already starting to see, back to your comment earlier, that physicians are starting to change. Not two, five, ten years from now. Now, AI is starting to impact them. In real time, could the AI make a diagnosis while the patient and the doctor are talking? It can. It can, right? It can start to take that information that's just taken into the generative AI as it's generating, and it can start to identify things about it.

40:50And especially it's powerful if it's connected to the rest of the record. So then it could say, you know, based on this visit and based on the patient record, we recommend a referral or we believe that there's, you know, the assessment of the chart now is that this person should go see a specialist or this person should try this pharmaceutical intervention. So, yeah, yeah. I mean, all of it's connected. And that's where we're going. Basically, we're going in a direction where physicians are going to get a lot more help. I think they've been very expensive data entry clerks for many years, entering data into these patient records, into these EHRs, and not getting much back.

41:34And now we're starting to see AI-driven co-pilots actually be able to pull all these patterns together and identify these kind of simple insights that help physicians now move things forward. It's like, think about a plane and a pilot. You get into a plane and you fly and you don't even think anything about it, but the onboard computer is doing everything, but you still probably would not fly unless that pilot got into the plane, right? We feel much better that there's a pilot there. I think this is where healthcare is going. You're going to have these co-pilots, all this machinery and intelligence that helps the physician make a good decision.

42:24But for the most part, a lot of the work, the hard work is going to be done by machines in the future. And that sounds a little much, but when you consider the fact that a human body has far more complexity than a Boeing 747, There's no way a pilot could run those planes properly without onboard computers. So how are they delivering patient visits, right? Like, let's say I'm a patient and let's say some days I have stomach aches, some days I have migraines, some days I'm a little bit more fatigued than others. And I go to a doctor, they might say something like, well, when you have a stomach ache, take this Pepto-Bismol.

43:05And when you have a headache, take Excedrin. And when you're fatigued, take, you know, melatonin. But you're saying I could go in and given my prior records and given the conversation I'm having with the doctor, the AI could say, no, you have this kidney problem, this rare kidney problem, and you need to see a specialist right now. So that's the sort of leap that could happen where the doctor is just not going to make the connection, oh, stomach aches, headaches, fatigue on different days, extremely rare kidney disease. The doctor might not make that connection because he's never encountered it before.

43:42But the AI, among its millions of records, has encountered it maybe many times. Correct. I mean, a lot of what you see when you go to a walk-in clinic or some kind of urgent care center is basically a physician treating your symptoms. It's like, okay, I'm sorry you're feeling that way here, a bunch of things that you could do. but there may be something going on. And from a preventative health, this is all about preventative health. If we can identify the issue earlier, time in medicine is everything, right? So if you're not feeling well, if you've got a pain somewhere, listen to it. Don't ever suppress that because pain is a signal.

44:18All these things that happen in your body are signals and you need to pay attention. You need to be aware, right? And what we're doing with the eyes, we're just getting time. And that's allowing us, that's giving us really precious opportunity to intervene before things get worse. Do you have a sense of what percentage of the time the AI makes a recommendation that's correct versus incorrect? Oh, that's a good question. The vast majority of the time, it's got a point. Now, sometimes the doctor could know, so it could just sort of dismiss the notification or what have you. Sometimes the AI, due to a lack of information, it may not make the right recommendations, but that's also in the quality of the AI.

45:03So if it doesn't have enough information, it shouldn't make a call. So we've sort of seen a little bit of that is that, is that, you know, it doesn't always have to give an output. It should give an output when it feels, you know, quite certain based on, you know, all the things that it needs to see in terms of pattern detection. So that does get to quality of AI. But, but, but, but I would say that, that with the type of co-pilots that we're building and that we see, you know, there's, there's a, there's a lot of, you know, rigor and discipline and quality that goes into the stuff. I would say, you know, it's sort of rarely wrong.

45:36You know, it's probably going to be right, you know, 95 plus percent of the time, or it have some kind of point to make. So you'd have to review those notifications as a doctor and try to understand why it's making those inferences. Are we getting to a point where we can even do, or you could implement healthcare as a service. So I could log on to heal well and say, oh, I have stomach aches today. And then tomorrow I can log in and say, oh, I've, I had a migraine today and I took et cetera. And then the next day, oh, I slept 14 hours for some reason. And then, and then it comes back and says, look, you need to see this doctor.

46:17He's available right now. I'm going to make the call for you. And boom. So the whole, so HASS, H-A-S, Healthcare as a Service. Absolutely. Yeah, no, I think that in the future, you're going to have a lot of intelligent questionnaires, AI that asks you a bunch of questions and basically going through a chat. And then it'll get to a point where it says, you know, I'm going to tee up a conversation for you right now with someone who can address this one point or talk to you about the situation. And the beautiful thing about that is that as soon as you speak to that person, it's already delivered kind of the assessment and all the notes required for that person to understand exactly the situation and where they can start to make a valuable contribution.

47:01Because I feel like now there are simple versions of that, but it's very rule-based. Like, you know, let's say it's specifically about sleep. Like how many hours on average do you sleep a night? Oh, okay, you probably need this drug. Boom. So it's very rule-based. Totally, totally. But if it's based on the AI of understanding the data of millions of patients paired with their diagnoses and then comparing my specific language and context to the AI database, it seems like that's very powerful. Yeah. I mean, look, I think the more history you have, the better the AI is going to be. And what's interesting, it's not just about your data.

47:43A capable AI is doing this deep learning where it's comparing your data with all the other data that it's seen. So part of this is about unlocking the value of your data, but in context with these greater data sets. Well, how many patients' records do you have in the AI? Oh, I mean, we have over 20 million patient records just in our Canadian business. So it's a pretty vast amount of patient record. I mean, it's terabytes of data. So it's like a doctor, like a really smart doctor with perfect memory on 20 million patients. Yeah, exactly. I mean, it's hard to beat, right? Jay, maybe this could be, Jay, do you think we have like a good part one?

48:26And then we will do a part two where I would ask more about the specifics of healthcare AI and your companies and also more about entrepreneurship. but I think this idea of healthcare as a service will get people thinking and uh and I'm very excited about the future and what it brings so thanks so much Ahmed and we'll talk maybe not next week but the week after we'll and Jay will help schedule something

From the publisher

Episode Description:In this episode, James sits down with Hamed Shahbazi, the CEO of WELL Health Technology and Chairman of Healwell AI, who has been pioneering advancements in healthcare through technology. Hamed shares how AI is not just a tool for the future but a current reality that is reshaping the healthcare industry. From predicting diseases to optimizing treatments, AI is making healthcare more personalized and proactive. Hamed's insights reveal how these technological advancements are poised to dramatically improve patient outcomes, streamline medical processes, and ultimately save lives.This episode isn't just about the potential of AI in healthcare-it's about the actual changes happening right now. Hamed discusses the importance of data in healthcare and how AI uses this data to make life-saving decisions that were previously unimaginable. If you're interested in the future of medicine, this episode offers a clear view of what's coming and what's already here.What You'll Learn:The Role of AI in Diagnosing Rare Diseases: Discover how AI is capable of identifying rare diseases that even experienced doctors might miss, using complex data analysis.AI's Impact on Personalized Healthcare: Learn how AI personalizes healthcare by analyzing individual patient data against millions of other records to offer tailored health advice and treatment plans.Efficiency in Healthcare with AI: Understand how AI is reducing the workload for physicians by automating data entry and patient monitoring, allowing them to focus more on patient care.Healthcare as a Service: Explore the concept of Healthcare as a Service (HaaS), where AI-driven platforms provide continuous health monitoring and proactive care suggestions.The Future of AI and Healthcare: Get insights into how AI is set to transform healthcare, not just in the long term but in the immediate future, making healthcare more accessible and efficient.Chapters:00:00 - Introduction and the Evolution of Healthcare03:10 - How the Oura Ring Tracks and Measures Stress08:05 - The Impact of Continuous Health Monitoring12:15 - AI's Role in Health Data Analysis and Prediction19:35 - The Future of AI in Personalized Healthcare26:00 - Real-Life Examples of AI Diagnosing Rare Diseases35:20 - Challenges and Benefits of AI in Medical Diagnosis42:40 - The Concept of Healthcare as a Service46:55 - Future Predictions and Parting ThoughtsAdditional Resources:Oura Ring Official WebsiteWell Health TechnologiesHamed Shahbazi | LinkedIn
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