The Consumer Home Healthcare Revolution with Shilpi Gupta and Steven Wardell

Speaker A: Hello welcome everyone. Today in our live audience to our show, Digital Health Investor Talk. The topic of today's show is the consumer home health revolution with our guest Shilpi ah Gupta. Shilpi is the founder and CEO of Quinn Labs, a hormone testing company that leverages cutting edge at home hormone tests and data insights to help users optimize their hormones and daily performance. This show is being recorded and will be included in our podcast series called Digital Health Investor Talk. Subscribe to our podcast on Apple and Spotify. This is not investment advice and we are not investment advisors. Here's the format of Today's show. It's 90 minutes long and we'll spend the first half of the show discussing the news and the macro picture and some other topics. And then the second half of the show we'll focus on our special topic of today which is about the consumers home health revolution. And we'll be taking your questions throughout the show in the LinkedIn text chat. So ah, welcome to our show and please introduce yourself to our audience.

Speaker B: Thanks for having me, Steve. Well, so I'm a, I'm a, you know, a robotics and consumer tech builder. I spent nearly two decades taking heart sensing platforms from the lab to people who actually use them like Roombat, a robot and autonomous systems. Autonomous robots at Amazon, neuromuscular sensing at Pison. And you know, through this journey, life and personal health really handed me a problem that has now become my mission to solve through Quinn. And the problem is that, you know, hormones are a, a key signal for body. And we'll talk more about that later in the show. But uh, they are measured as snapshots and um, only you know, very rarely or once or twice a year doesn't give us the right picture of our health. And there's a lot m more we could do there for ourselves. And so we're building Quinn and we are building the measurement layer of our endocrine system. We are starting with a tiny blood drop as the clinical ground source of truth. And long term we turn that longitudinal data into passive sensing through a wearable that we're building towards.

Speaker A: That's great, wonderful. Uh, sounds very exciting. Well, so now we'll move to the part of our show that's the macro news part. And here we talk about uh, you know, news in the, in the press about how the economy is doing and especially how that affects what I call the innovation economy, especially in health care. And that's young company leaders and investors. And so for our audience, feel free to throw questions in that you have in the chat. So I guess I'll start with the economy. It is now and it continues to be and has been for a while, kind of a tale of two cities. So if you're as part of the AI boom, then you're probably looking around saying things are looking great. And if you're not part of the AI Boom, then things look like, you know, we might even be in a recession or something. And if you look at the uh, Nasdaq, it seems to be doing great and it was at, it's at new highs, uh, recently. But if you're, if you are not, if you're not looking at the Nasdaq, you might be worried about inflation and, and, and other, other parts of the economy. So, um, what's, and, and for young company leaders and investors, they tend to want good economic conditions and stability and low inflation. And so it's a, it's a mixed outlook. They like high stock prices because that means when you're building something, when, when it comes time to exit, that implies that you're going to still have stock, high stock prices at exit time. So that's. So it's kind of a mixed picture. But are there any numbers that you follow, Any thoughts on this Shilpi, Any numbers that you follow and how's it affecting your world of, of starting a young company?

Speaker B: I think the big trend that I'd say that we m. Are following and I think we're all seeing is around investments in AI and how the trends around AR AI are impacting, uh, investments in young companies. So I mean, I'll say I'm not a macro strategist, but I would look at it from a founder point of view, a founder lens. And you know, there's a real shift here in how we judge the durability of companies and therefore what we invest in. And I is, you know, you see a lot of investments around AI and AI labels. But uh, I'd say that durability really is coming from what is underneath that AI layer and what models cannot reproduce. And uh, so we'll talk more about data and how the data infrastructure is becoming strategically relevant to the economy overall and the enablement of the new economy.

Speaker A: There's a very interesting debate going on because these days a entrepreneur can show up to a VC and say, look, I built my product and they built their product yesterday. And what does that mean? It used to be there. There was a rule of thumb that anyone who's building software, who's ahead in building software, has a real lead because somebody else you give them twice as much money, they can't really make up the ground if they're two years behind, they can't. With twice as much money, they're actually not going to be able to make up the ground very quickly and, and catch up to the person who's two years ahead. That's an old rule of thumb. But the new concern is that it's easy to build software. And so then the qu. Then the new question arises. What has durable value that you can build a company around? And some of the theses I've heard, there are claims that, you know, that it's shifting away from the having a software product and towards having great sales, but that's, but not everyone agrees with that. And then finally there's also. Do you have ip? Uh, IP would be great. Do you have proprietary data? Proprietary data would be great. So the debate is sort of shifting and people are looking at, I think this is an unresolved debate, but those are some of the ideas people have of where kind of the durable competitive advantage is moving to. Uh, do you have any thoughts on that?

Speaker B: Yeah, for sure. This comes up a lot. I think what you mentioned around the moat moving to proprietary data and, you know, distribution or sales is definitely relevant. It comes up a lot. I think a couple of other things that are becoming very relevant are actually hardware, uh, and, you know, scientific risk has become a feature in companies as opposed to something that people used to sort of run away from and it was considered too hard and too unknown. Now that has become more of a moat and way more attractive. We call it physical AI, but also in health tech, I think clinical evidence, um, you know, workflows, regulatory positions, these are things that are also pretty strong, strong proprietary positions that differentiate companies. AI, I think right now has become more of a, um, operating system, kind of a common denominator. It's really some of these things that make a company durable, investable and brand equity. I think one that I missed is directly related to distribution, but something that we underrate usually. Um, I think factor that'll add, which is kind of builds on your point about, uh, you know, proprietary data. The key question that investors now ask is what do you own that foundations cannot? And I think my answer to that is AI can interpret what we measure, can do a phenomenal job at that, but it cannot recover a signal, for instance, a hormone curve that was never measured before. So the measurement infrastructure is a key differentiator.

Speaker A: That's really interesting and I like that you brought up brand equity. Uh, so, because you're building a company that has to deal with, with the challenges of health care but is also a consumer facing company and is, and is building a brand. And there's a joke in the investor world that healthcare investors, you know, or sorry consumer investors can't, can't spell FDA and healthcare investors can't spell brand. And so that, that's kind of a, a so, so you, but, but you're, you're this fusion area between two areas where you have to figure out how to both do something that is credible in healthcare and also has a brand with consumers as well. So let's see the. But that, that is interesting that in the AI age that brand may matter more. I hadn't thought of that. So very interesting. Well, so now we'll move on to the next section and for our audience, feel free to throw any questions uh, you have in the chat which is just public policy changes. And so are you, are you, are there any public policy changes that have come up in the last few ah, months that you're following that that matter a lot to your part of healthcare?

Speaker B: Yeah, definitely. We're watching a couple of trends uh, around policies especially around the fda. So two recent FD shifts uh, really matter to our category. One is that the FDA has begun approving the revised menopause therapy or hormone therapy labels and removing you know, some broad boxed warnings, risk statements from these products. And so as of early this year, so they announced this late last year, early this year, they actually put it in action. They've already taken care of a couple of products and removed the warnings. There are so for instance, you know, risk statements around cardiovascular disease or breast cancer or you know, potential dementia. These were some scary statements that were on hormone therapy, just broadly put erroneously and have been removed by the fda starting with a couple of brands and they're continuing the process. There are nuanced labels that still exist which matter and they should. So I will put that caveat in there. Another really interesting trend or policy trend that we are following is their uh, the FDA's General Wellness Guidance. And so they have, you know, made it a lot clearer their position a lot clearer around low risk wellness products and really matters for founders like us because we know what to work towards. So for example this year, you know, the closeout letter from the FDA for the WHOOP case was very insightful because what happened there was as WHOOP changed and you know, revised the labeling on their product. You know that feature came under the FDA wellness guidance and that was a very interesting case. To follow. It really sort of brings a lot of clarity in that space.

Speaker A: And do, you know, so any of the particulars of the whoop case, what, what was it that they, that they did that caused, you know, the FDA to have to talk to them and then ultimately write, write a letter?

Speaker B: Yeah, at a high level it was around blood pressure sensing and uh, you know, they got a letter around that. But really, long story short, what it really came down to, what was the claim? Not necessarily what is the feature? So that is sort of the distinction that allowed for the final resolution of the case. And you know, I think this is a learning for all of us where if you're making a medical claim that is of course very different assessment from the FDA versus, you know, if you're. The way that you, you're using your technology and the product and how it is being used is, you know, not anything related to therapy or diagnosis or, you know, impact safety is a. Altogether. So how you position it really matters.

Speaker A: Yeah, thank you. And I, I remember when Fitbit was doing some trailblazing and they were going to use, you know, light on, um, the wrist for heart rate variability measurement and there was, Things were not. It was not clear whether this data was going to be useful. It was not clear whether consumers wanted this data. It was not clear what kind of claims they could make. They were not going to make aggressive claims, but they were what they wanted to make, you know, sort of very, very, very minor claims. It was not clear what the FDA was going to do about that. And the end result is that people really wanted an affordable, convenient, uh, you know, heart rate variability monitor on their wrist, which is not, not the ideal place to put it, but it is the ideal place to wear a wristband. And, and I think it's done. There's been great good done for consumers and for the wellness sector and also for knowledge about health care as well. And it has not, you know, it is not in general the companies that have followed in its footsteps in this category, you know, have not been abusive and have not made, made excessive claims and have not run into trouble with the fda. So that's, that's an, that's an area where I hope we'll see more of that when it comes to hormone testing and other sensors. There's other sensors that are, that are coming down the pike, like measuring stress and measuring sunlight and other measures. And I think, I hope we'll continue to see, you know, more, more diagnostic tests and more sensors out there, uh, to, to grow this category. So yeah, well, well, great. Well, so now we're going to move on to another category and for those of you in the audience, feel free to throw any questions in, in the chat as well. But this is on, um, the fundraising environment at the early stage and also at the, at the later stage and consolidation, uh, M and A IPOs valuations. And so here, interestingly, we're seeing a strong IPO environment. And the ultimate example of that is SpaceX going public and achieving a valuation of around $2 trillion in the biggest fundraise IPO in history. And uh, also stories both of OpenAI and Anthropic rushing to IPO, but maybe also delaying their IPOs, so that there it could have been this summer or early fall and they may now be later than that. And that may be in part due to the challenge of Chinese open source models that are calling into question the ability of these companies to charge a lot for their frontier models, which is an interesting question. And it also means that they may seek to, they may be seeking to race to IPO to get it done and then they may have bankers coming to them and saying that the investors they have in mind are getting cold feet like the Fidelities and the Blackstones and the blackrocks of the world and that sort of thing. So now we've seen this. The IPO environment is strong, but it's weak in digital health. And so this is maddening if you're in digital health. So we had about three IPO two years ago. So that was, there was, let's see, the diabetes company, um, which I'll take its name in a second. And, and there was Heartflow and Omada and Hinge. Omada's a diabetes company.

Speaker B: Hinge was one.

Speaker A: And then we, and then we haven't seen anymore. And there's speculation why we haven't seen anymore. But this is really bad news for a number of reasons because it suggests to digital health investors that if they invest in a company that it can't, it can't go public. That's one reason. And then those companies that are public and that are out there that are considered to be bellwethers, and that would be like Teladoc or the acquisition of Accolade or Hinds is another bellwether out there. They're trading relatively low. So they're trading at, at 4, 3, 2 times revenue. And uh, this is also of great concern. There's venture investors who've put, invested a lot of money in companies that are unicorns that would like to go public and that are not able to go public. And so, and so this is a concern now. At the same time there is scuttlebutt that there are three possible so called digital health companies going to ipo. And that's a whoop. Aura and Strava have taken public steps to ipo. But they are consumer companies, they are not in the healthcare system, they're consumer companies. And so if they go, if they can go public, if they do go public, this would be great news for every consumer health company out there. Wellness companies that they too could get high valuations, they too could go public. Their investors can exit relatively easily. But I think it would not carry positive contagion over into digital health, which is usually healthcare, which is tech companies that inside the healthcare system that sell into healthcare. That's that sort of thing. So we're at this funny moment, the IPO windows open but not for digital health. But suddenly there's a lot of interest in consumer, we may see some great consumer IPOs coming up. So that's kind of the setting the table for what's going on in digital health. Do you have any, any, any thoughts about what you're seeing? And, and then we, we do see a lot at the venture stage. We see a lot of venture interest in, in AI companies where, where they're bringing something really new in AI and we're seeing a durable trend in longevity as well. Consumer longevity. That's that, that, that's an interesting trend. But we're also seeing that a lot of companies that were, that were darlings of VCs two years ago or they're not, if they're not part of the AI wave, they're finding that it's just a lot tougher in today's environment to get attention and to raise money in today's environment. So that's some of what we're sort of seeing in the, in the environment. Any thoughts, Shilpi?

Speaker B: A couple, I think. Um, you know, in addition to sort of the IPO scenario holding up money that you're seeing, what we're also seeing is concentration of capital. So you know there's been some mega deals. In 2026, I was looking up numbers, you know, 40, 45% of the dollars of. So there's been 7, 4 billion invested in digital health deals this year. Right. 45% of them went to 8% of the companies. And so what this really means is that this cap, the capital is being concentrated and if you further dive into it, what that the big checks from investors are going to companies that are later stage and are, you know, potentially clear winners you would call them. Right. So I think that this is something for seed companies to look at and watch and understand what it means for us. And this is how I've internalized it as a founder, right, which is as a seed company who is raising, you know, our goal should be to try and mimic a later stage company. It should really be to define our moat, what is our differentiating science or technology approach. And then what is a cap is capital efficient de risking plan to get it to market. So if we're able to show that. So for instance for Quinn, right, we're building continuous hormone sensing starting with a blood device. Our whole plan is around finishing up the Generation 1 device and validating it, running pilots, generating the longitudinal, uh, to unlock the wearable and showcasing that to our investors, creating value along the way. That is our due scale plan. So, so sort of understanding, you know, what are those sort of parameters with, with the investors, making them comfortable is important also. Well, one good news is you talked about IPOs and you know, a little bit of dearth in, in the digital health market there. Uh, but good news is a women's health company has reached the public market. So Maven just filed to go public. And this actually goes to your question about durability. Right. And what tells me is what really is creating a durable sort of creating durable value is companies that own that trusted data layer and that relationship. Right. So AI. I know that you know, we're talking about. All right, AI is the new hype and you um, know the darlings are no longer darlings and because of the excitement around AI. But I'd say AI is not a strategy, it's really again an enabler. Right. So going back to what is it that you know, the company can do that a foundation model cannot own is the critical question. And if I have to sort of boil this down to, you know, so what should be our framework as companies to play in this market? I, you know, I really see this as a creating a flywheel effect with you know, you think about three parameters and create a flywheel effect with proprietary data, a long term relationship with your customers and a platform that can expand into adjacencies. So what does that mean? You know, you, you companies that are, you know, have done really well recently, like Health Snap, I think you're trying to recall this name which was, you know, it's monetizing relationships in between visit relationships. Another company that I can think of is Quant Health, right so they're about biological data, but really sort of compounding impact with AI. And we talked about hinges acquisition of cylinder, but really what that allows them to do is move into an adjacent space and create exponential value. Uh, so this flywheel effect is what's creating durable companies. This is where, what is investable today. So again, going back to what does this mean to me as a founder, as a seed company looking at. All right, well, we're the, based on what we're building, we know that one time tasks can be commoditized very easily. Can be commoditized easily. That's not what we're building. What we're building is a relationship with our customers by giving them value with longitudinal data, their biomarker data, syncing that up with their, you know, their wearable data that already exists over time, syncing that up with symptoms and treatments and outcomes, expanding into adjacencies and spinning that flywheel into growth. Yeah, so I guess market to founder story right there.

Speaker A: That's great. Very interesting. Yeah, so that's kind of, it's bad news. I think part of what's happening here is that VCs find the market changing quickly and confusing. And so they're doing mega deals with companies they think are already on the path to winning. And that's almost not their job. Their job is actually to get in there early and take the risk. And so, and one VC explained it to me and said that they had investment themes they liked in the past and those no longer work. And then they don't have new investment themes they like today. And so. But some funds you actually see are jumping in and making aggressive investments today. So I'm thinking of Springtide in the early stage of the digital health world. And also Andreessen Horowitz is being very aggressive about investing in early stage digital health companies, even though sometimes one of the, you know, one of the hyperscaling model companies will just announced they're doing what Andreessen just forced just back to company to do or whatever, which, um, is usually really bad news for that company. So anyway, but that's really great. Thank you. So next we're going to look at business and trade journal stories. And so for our audience, feel free, feel free to throw any of these that you want us to comment on into the chat, but just stories that, that caught your attention that you were interested in and in the last few, uh, months. And so I think one interesting story is that OpenAI and anthropic seem to continue to be pushing into Healthcare, this is clearly an area that they care about. I think that OpenAI both said that it was going to continue to push into healthcare and pharma, both strategically. And they also said that this would counteract some of the damage that's been, the PR damage that's been done to AI. So AI you have, um, there's an interesting PR situation of AI among the general public. If you're talking about people who used it instead of Google Search and got a better result and they like their better result, then love that AI. But if you're talking about the AI that is, that is drinking up America's rivers and putting everyone out of work, then people hate that AI. And so, so, but Anthropic was talking about how they're, they're pushing into healthcare and they think that by delivering insights about new drugs to cure disease, for example, that they can sort of turn the PR problems that AI has around. But big picture, the hyperscalers all see healthcare as a very important area for them. And I also saw that Novo Nordisk and Amazon Web Services have announced that they're launching an AI drug discovery hub. So that's great news and important. And I know that a lot of young companies are trying to make AI better AI drug discovery tools and a lot of pharma is trying to build those tools in house right now. That's a hot area of trying to apply, interestingly, among other things is applying the pattern recognition of generative AI to genetic code instead of to words in books. So computer code, generative AI is very good with human language, it's very good with computer code, but it's also very good with genetic food as well. It's very good at, at being able to analyze it and being able to, to code new things with it. So also, and I'll just mention, you know, one, uh, you know, one fundraiser I thought was very interesting was to see that health Snap has raised 25 million for its AI virtual care platform with Eastern Capital Partners leading the round. And so AI virtual care platform, that sounds like we've seen a lot of companies like this. And so I'd say that this is an example of the AI trend and also the virtual care management trend just being sort of amplified through the latest investment round and then Hinge Health acquiring Cylinder. So there has been under consolidation in digital health, which is to say that there have been over. There have been several thousand Companies started since 2009 that are venture backed in digital health. And I have said publicly repeatedly over the years, I've said it in 2015 and 2017 and 2019 and since then as well, we ought to be seeing more consolidation in the sector. And we've seen weak consolidation, uh, instead. And so here's a case where a New York Stock Exchange treated company in child. It has a relatively high valuation multiple and that means it has a hot currency it can use in acquisitions. Has chosen to buy Cylinder as a product to fill out its product portfolio. This is great news in a number of ways. First of all, we have under consolidation in the sector and this is an example of consolidation catching up a little bit. And it also means that Hinge is going to be a consolidator in this sector. Hinge used to be a young company. They went public, they have two currencies, they have the ability to access the capital markets easily as a public company and they've made one acquisition. I think that if this goes well, we can expect the management team to have the support of its investors to make a bunch of acquisitions and the sector needs that. And then they're going to go from being sort of a builder company to being a consolidator company. Uh, and that in turn will kickstart a wave of consolidation in this sector. They're in the employer health benefits sector and that's the sector most well suited to a future roll up and consolidation. So that's. But any thoughts on, did you see any business news, do you have any thoughts on these stories and did you see any, any business news stories that you want to bring to our audience's attention?

Speaker B: For sure. I think, you know, you're spot on about, you know, Hinge and, and the consolidation that is much needed. I'm going to sort of add to that and say going to, you look at consumer health, right there is growth through partnerships, a ton of that going on. So you know, companies like Whoop and Aura for instance are becoming, um, you know, they're growing significantly by becoming platforms of health. You know, Whoop's partnership with Quest for Blood Panels for instance, in partnership with Natural Cycles, bringing in women's health really strongly into their portfolio. Same thing with Aura, uh, a partnership with Dexcom. So that's another very interesting trend to watch watch. And again going back to this all kind of ties back to the, the, the creating that flywheel effect of proprietary data doing what AI is, you know, cannot extend with uh, extend to which is that measurement layer. I'm happy that Anthropic is announcing getting into healthcare because AI enhances a lot of that analytical need and is supports these Vast sort of, you know, uh, processing these vast data sets and opens up new opportunities for drug discovery or diagnostics and therapies, but can't run on a, on a, without a foundational biological layer. So that is another opportunity that is growing very rapidly in the space.

Speaker A: Thank you. Well, good. So now we get to a part of the show which is about technology use in our lives. Uh, and so, and uh, this is, this is sort of a crowd pleaser part of the show, but where we talk about is there a technology or a use of Gen AI that has improved your life? And so I'll mention two here. One is that a really interesting modality of using Gen AI in your life is what are called the live voice modes. And so this is where you're talking to a voice M model and one of the early ones was OpenAI's voice model. And I have just noticed in the last month that OpenAI's has just gotten much better. So it's worth checking out again if a lot of us, we like our big screens and our big keyboards and we like, you know, our workflows that are desktop based. But it's worth checking out OpenAI's voice mode. And I'm talking about the live voice mode, not the mode where it just transcribes your words into text so you can hit enter on it, but where you're having a back and forth conversation. And something I've noticed. So when, when this voice agent is talking to you, it's just more realistic. The voice is more sounds more human. That's very nice. But there's something about the manners that has gotten better recently. So it knows that if someone, if a third party speaks up in the background, it knows not to just stop and go, go silent or something, which is what, what a lot of them do. But it's learned, oh that that person's not part of the conversation. And I'll just keep answering my question to the person who asked the question or just now with a friend I openly, I was getting a long answer to a question about sports rivalries and then I interrupted it and said, but tell me about this specific sports rivalry. And it switched instantly telling me about that. So there's something about the manners has just gotten better, more usable. That's the first I'll mention. And then the second is that I'm doing kind of an experiment of I'm trying to use Gemini inside of every Google product. So I use the Google productivity suite, not the Microsoft Productivity suite. And then Google has gone to great lengths to put Gemini inside of every product and so then I'm using it inside of every product. So I use it in inside of Google sheets to fix a formula and I use it inside of Gmail to write something and I use it, I'm using it in all of the Google products as opposed to an alternative would be to use Claude and to export something from the Google workload workflow system, put it in Claude, do an operation on it, then take it back out of Claude and then put it into the Google back into the Google workflow system. So I'm trying to use things in Gemini in context in Google and that, that, that's going relatively well. And uh, and then there's a tip as part of that because I found that one of the things that Google has done is they've put Gemini in Chrome and so you have your browser and you're working in your browser all day and then you have a sidebar that is Chrome and, and you can type in questions in Chrome and the key is that it can see your, your screen. So if you are asking it what's the expert functionality in this screen to do this extra thing? Uh, and I'm in HubSpot right now, right I'm in some productivity software right now. Then Gemini probably knows the answer. It's probably better than HubSpot own help. Nobody wants to know insights about HubSpot's own help but read an article and try to try to navigate their help system and but it knows the answer and it is looking at your screen and so it knows where you are, it knows where your cursor is. And I just found that to be wonderfully helpful. So um, Shelpi, any thoughts on that or did you come with an example of using technology in your life that has made your life better?

Speaker B: My goodness. Uh, a couple, I would say for my life better, my team's life better. There's no question about the fact that we are a small and mighty team running at a productivity of hundred x because of AI. We are an AI enabled AI forward team and so much so that when Fable was uh, you know, announced and retracted and came back again right after it got passed through regulatory, you know we, we all are working on app and we, you know, the team sort of is, we joke about our sleep data because we're sort of integrating that with hormone data every day and we all saw uh, you know, a dip in our sleep the day Fable was announced and then it spikes up again and it dips down again when Fable comes back so, but it is so powerful where, you know, we are able to use, you know, things like hooking up, for instance, you know, our AI to JIRA and managing our ticketing system. So much so that we have a full, full blown project management system and a smart one at that, without having to spend, you know, phenomenal resources on, on, you, uh, know, program management, which is an entire function in organizations typically. Right. So if I have a ticket that I've pulled and someone else wants to pull that, you know, pull the same item and work on it, you know, our AI is smart enough to say, not just say, hey, this Shilpi's opened it, but also, you know, tell them what could be the implication of them changing certain things in that particular item. So, you know, it is a huge enabler, an example of a huge enabler for the organization. Uh, and so much for, you know, for instance, content creation and, and managing our, again, managing resources while we're trying to, when we output clean, beautiful branding that, you know, judgment is everything, though I would say humans is, humans are behind all of it, right? And you can't create it with the right judgment. But having the power of tools, not having to hire like a team of 10 to do the same thing is pretty phenomenal. Another one is, I think this one, this one makes me chuckle, but it's really empowering is my AI assistant who manages my calendar is as simple as copying Howie to my emails and saying, hey, just all I have to say is set this meeting up and it goes back into the emails, reads the context, who's on the email, what meeting are we talking about? And it sets up, you know, you know, shares my availability, gets theirs and sets it up in my Google Calendar, finds conflicts and deconflicts the calendar as well. So, uh, you know, it again, like, not a luxury I would afford at this stage. So very, very, very cool. Lastly, I would say, you know, I am a wearables lady. I have mine today, but I have more. But in, you know, as a person who's been tracking my health for a long time, what's really cool now is that both Whoop and Aura are starting to release women's health features and their agent embedded in the um, app is becoming smarter about the types of questions it can answer and how I can look back into my data and know my context. So I think that's, it's super cool to, to see that.

Speaker A: That's really interesting. Yeah. And so Howie, the scheduling agent, that, that's a really, really interesting. I think I'd Say for our audience, you know, if you, if you make lots of external appointments and you find it a hassle to do so. Check out Howie. And, and then, uh, I, I thought I'd also bring up. There's an interesting story of an agent run amok. And I think this is, so that this is a news story from the last couple of days. And so someone told a story of how they are a member of a gym. And the gym puts up a calendar of classes in exercise rooms and it lets you book a class out for one week. And then you can't book classes that, that are, that are scheduled by the, by the club, but you're not booked them more than a week out. And the point is, is that it wouldn't be fair for someone to go in and book every single class for six weeks or whatever. And so you have, you have to keep, you have to have a discipline of keeping coming back and looking at your schedule and booking something within the next week. And so one guy tasked his agent to go and book a certain class that was recurring at the gym for him. The, the agent went into the administrator somehow got into the administrative back end of the booking service and there was no limit on how many of these classes you could book. And so the agent booked him, uh, you know, in every class going out as far as, as, as was available as the class had been scheduled. And then the agent then also allegedly in some of the classes the agent booked him, but he was put on the wait list. And so the agent allegedly went in and canceled some of the people who were had already had previously been accepted into the class, which then automatically lifted and admitted him from the wait list into the room to get him into the class. And he then, uh, told it, well, don't do that, go back and undo it. And the agent said, I don't know how to undo it. And then he reported this to. So I think we're going to see a lot of those things happen. And a lot of people have pointed out that, that agents can simply not understand what you mean. And the example that's been used is like you tell an agent to cut a sandwich in half and it cuts it the wrong way. And you say, no, not that way, the other way. And then it cuts it in half again the wrong way. And you say, no, not that way. And, and then it's cut to the half in the wrong way. Whereas a human with no context and would know, you know, when you say cut the sandwich in half, they're going to get it Right. The first time. But agents don't and people don't know to show them the context or maybe even lack the language to tell them exactly what to do in every particular. And so I think, I think we'll see more of this, both the low stakes accidental kind and also perhaps the high stakes malicious kind as well. So hear that story about the gym.

Speaker B: It's pretty crazy. Yeah, there's definitely some stories like that and the sandwich is a great example. There's uh, the way I look at it, you know, there's, there's two ways to go about it. One is if you preemptively understand how AI works, you sort of, you know, you know, design your language to talk to the AI the way it would. You um, know, give you the best results but then challenge it against itself. There is, it's, it's a process that's great.

Speaker A: Well, so the next part of our show we're just going to talk about any conferences that are coming up that you're excited about and, and if you recommend our audience go to the conference. And so I'm, I'm going to mention two conferences, one here in Boston, the MGH World Medical Innovation Forum coming up September 22nd, 23rd. This is Boston's showcase of, of health, health innovation. So and with a, ah, with a, an emphasis on the kinds of research and the kinds of procedures that are done in academic, medical, in leading academic medical centers like mgh. And then the other is, I think people are now gearing up for the health conference. This will be an interesting year for the health conference. The conference has been purchased by a new conference company and that'll give it benefits of that. The company runs many conferences and there'll be some synergies across those conferences. But there's also a concern because the original conference builder, a guy named Jonathan Weiner is, it was a genius and ah, he's now sold to this company and uh, people kind of liked his product and they're wondering what direction the conference is going to go next without Jonathan Weiner associated with it. And so, but it still is, you know, I would say the best innovation conference and one of the best investor conferences for young companies in digital health. A lot of the flagship venture funds not only attend but also sponsor this conference. The digital health venture funds, uh, like Flare or Oak or others, they'll be, they'll have most of their partners there. They'll be doing meetings for the entire conference there. So it's a good conference to, it's an expensive conference. Some people I think, I think they don't let you buy just an Expo Pass, but if they do, you could consider buying just an Expo Pass. The ticket's expensive. Some people hang out in cafes nearby. This is usually in, like in a giant hotel casino and not the whole giant hotel casino. So people will find another part of the hotel casino to hang out in and do their meetings in, which may mean that your, your guests have to walk 15 minutes inside a single building in order to get to you. Uh, that, that's how the Las Vegas casinos work. And, but it's good to see what your competition is doing. It's good to get a sense of what innovation executives are seeing and doing. Um, it's not a great trade show. It's not a great place to like. Uh, HIMSS is a great trade show for hospital CIOs. You can meet hospital CIOs, you can try to sell the hospital CIOs there. Health is not really. It's more of an innovation conference and investor conference. It's not really a trade show. And it's a lot of, a lot of meetings with innovation executives and with investors at this conference. Uh, so I'm looking forward to it. They usually do a great job of, of being fun and informative and you can get, you can get a lot of meetings done a day. You can get four to eight meetings done per day at the conference. So, but people are a little concerned that it'll lose some of the magic that it had in the past, uh, and become sort of more, more institutional as a conference. So, and I noticed that they seem to be moving this conference a little later in the year. So now it's going to be November 15th. That, that, that's right around the time of thanks Thanksgiving is coming right up. And I think this is because their intent is to be a JP Morgan killer. So the JP Morgan HEALTHCARE conference every year is second week of January in San Francisco. And a lot of people don't like it. They don't like it because it's a crazy zoo. They don't like it because they can't get tickets from J.P. morgan to the conference that's inside the Weston St. Francis Hotel. They don't like it because if you don't have those tickets, there's no programming. It is a Wall street investor conference. It is not a trade show or an innovation conference. There are, it's a three ring circus. There are extra rings of the circus, um, et cetera. But, uh, it's rainy and cold in San Francisco. People get Their business clothes and their umbrellas wrecked in the wind and the rain walking between hotels in San Francisco. So there's a lot of people don't like. Uh, it's absurdly expensive. San Francisco has in the past had some problems with crime. And in that very area, Union Square area, that San Francisco may be on the rebound. We'll see. But so Health is designed to be a fabulous conference all inside of a single Las Vegas casino that has wonderful programming and plenty of great meeting space. Uh, and so m. They keep moving it closer to JP Morgan, I think, so that people spend their budgets on Health first and then they don't have any more meetings left to make and they don't have more budget left when it comes time to go to JPMorgan. So that's my review of upcoming conferences. Shilpi, any thoughts on these conferences and any conferences you're really looking forward to?

Speaker B: Well, that was an interesting take. It was very, very, uh, yeah, insightful. Well, I'll be at Health, so, you know, if you're building in the space or investing in space, I'd love to talk. I also been actively involved now with the Massachusetts AI Coalition and they have plenty of events that in the area. They were part of the Boston Tech Week as well. And the ecosystem is coming together beautifully. There's plenty of. There's actually some, you know, founder dinners and events that bring the ecosystem together. So, yeah, just planning to be actively a part of that. Another one, the, uh, and by the way, young companies like us, if you become a member and you know, there's a whole process to go get to become a member, you know, they, I think they last. They said they had some 200 applications. They accepted less than 50 or something like that. And so, you know, but if you do become a member, then you get GPU credits, you get, you know, space free space and a couple of other perks. So it's an exciting time to be a part of this right now. And with that, you know, the events and conferences that they hold, uh, another one that we find to be very impactful is a virtual conference called Women of Wearables. And this is actually run by somebody out of Europe, but it's a global conference. There's thousands of women that who dial in for these, you know, webinars and virtual conferences. Um, they do a really good job of highlighting, uh, you know, up and coming founders. There is one that's coming up in the fall that is about women in AI and uh, yeah, so very, very, uh, meaningful community that we find to be useful, to be a part of.

Speaker A: That's great. Wonderful. And then any personal notices. So I think uh, for my personal notice I'll just mention that our next show's coming up Wednesday, August 26th with our guest Sage Kanuja. Uh, who's we talking about? The new patient journey in the age of AI and how certain institutions like employers or drug companies will want to build navigators, AI navigators to help patients in their patient journey. And so for that, you know, just uh, look at, look to my emails and the Eventbrite website to sign up for that. And Shilpi, any personal notices for our audience?

Speaker B: Well, Quinn is as, is as personal as it gets for me. So I'd say we're raising our seed now. That is our news and we're raising it to complete our at home blood platform and run our first pilots this year while advancing our wearable in parallel. So if you invest in deep tech or, or digital health or uh, you know, you're a potential strategic partner, a clinician who wants to help build this hormone intelligence category. Please find me, come find me. I'd love to talk to you.

Speaker A: That's great. So now we move to the next half of our show which is about the consumer home health revolution and consumers now able to track their health at home. And so let's just start really big picture. What is the new consumer revolution in health?

Speaker B: Wow. So I think there, there are. The revolution consists of a couple of major things happening at the same time, major trends, waves happening at the same time. Uh, the first as we see it is the mindset shift. Moving from the sick care model to preventative. Instead of fixing something that is broken and showing up once in a while, moving to staying ahead of problems and optimizing health for the long term. Right. So that's sort of one thing that is starting to change behaviorally and from a mindset standpoint, another one that is an enabler of this trend, but also an independent one is the democratization of data. So measurement is moving out of the lab and the clinic to the home. And just as importantly the interpretation of this m these measurements or this data, uh, is becoming very relevant to people where they're tracking their own health right over time and making lifestyle decisions, optimizing their health span longevity without waiting for sort of what we would say, waiting for top down medical permission. And they're far more open to good, uh, or bad, open to self experimentation than they were five years ago. So uh, you know, I think this all comes from that democratization of data. And of course there's the AI trend, right. So overarching trend of AI. But what that is doing is providing a very easy to use and understand interface for all of this data that is coming to light. And you know, instead of. So for instance, you know, five years ago where if you were a hardcore person in health, right. And maybe an early adopter of this kind of data, you'd be looking at a massive spreadsheet which is not actionable for 90% of us. And now AI has changed that, making those trend relationships, the personal baselines and customizing, uh, the interpretation has become an enabler of this trend of optimizing help from home. Yeah. So I think these sort of the big trends.

Speaker A: You mentioned that 90% of us don't really know what to do with a big spreadsheet that might have our test numbers on it. So I, I know people who have gotten a, uh, test results for a uh, blood test that had multiple test results in it. And then they take a picture and submit it to their AI. And it gives them, you know, and it takes the headache out of trying to think about it is this high or is this low or how or that sort of thing. So in fact that's, that's the first thing a lot of people do when they get, if they, they could get a medical test result, they could also get a bill, a medical bill. And they, they have their AI interpret either one. So I think when people think of home health testing, they probably have an outdated view in their mind. They might think about, of a pregnancy test, maybe purchased at cvs. Uh, for example, they might think of STD tests because that's something that they would rather do very privately and their doctor would love to talk to them about STD tests, but they, they frankly don't want to talk to their doctor about STD tests so that they'd love to access that at home. And so, but that's not the. But people don't know what's really possible today. And uh. So can you tell us about the change? What's driving the change and what does it mean today?

Speaker B: Yeah, that's interesting observation. So I'd say the pregnancy test was an early proof that hormone measurements can move out of the lab. Right. And become sort of an at home, simple, trusted consumer, uh, action. Um, I think what's changed now is, you know, and all because of a lot more sophisticated technology. You call for sampling, you call it, you know, more compact analytical systems, assays, connectivity software, AI modeling, you know, massive changes in technology. Have made all of this sort of this the single test that used to be the case and you know, the only case become a potentially much richer experience. Not just a, ah, yes or no. And so sort of seeing this big biological trend over time brings a different level of value to people. And I'd say, you know, Covid normalized a lot. You know, I think that was sort of the inflection point really for normalizing at home tests where people, you know, where you found it okay to test yourself at home. And wearables have normalized us looking at our data. Right? So when you look at these two things, what has now become the norm is for people to expect to see their numbers and their trends. And so, so this where we see, you know, things have come from that, that single pregnancy test. However, I will say me coming from my, my consumer background, I get wary of, of early technologies in the space that you know, may have sort of a working assay and they create, call it product. Because I truly believe for something to be an at home solution and a meaningful actionable solution, you know, it must be seamless in terms of sampling, in terms of calibration or error handling, right interpretation. And there is still some ways to go there for the technologies to mature and to become real. So for that consumer level usability and having that practicality. So, so for instance in our space, you know, when you look at uh. All right, so what has changed from those like pregnancy tests to an STD test to now what still only what exists right now, what is status quo is hormone, hormone. Well, urine trackers. That's what exists today for hormones. Those are inferring hormones from metabolites. And then you have Malin blood tests which again is a single snapshot. And so, so that, that there's still a lot of work to be done to change from there to something that I just described as a longitudinal biological layer where it is meaningful and actionable day to day following how your body changes. Um, and that is to come really interesting.

Speaker A: And um, have there been any sort of developments in making more different kinds of testing available and making it cheaper and also making it more frequent or possibly even more continuous? Uh, what are the sorts of developments we've seen on those fronts?

Speaker B: Yeah, there have been and I think the developments have been around, you know, what assays and ah, what are, what are the technologies surrounding, you know, assays? What are the sampling technologies out there? Something that's really interesting now is micro sampling for blood. For instance. You have these devices that have showed up in the market that were originally, uh, designed for little, little ones that make it seamless. And now mailing tests have become sort of enabled because of that. Dry blood spots were not a thing a couple of years ago, and they've really taken off after, after Covid, the lab equipment now has become more sophisticated to, to process even very low concentrations like picomolar levels, things in our, in our, in our biofluids. So there has been a lot of change in technology. I'd say what is an enabler of bringing all of this to the home is not just one, but multiple changes that allow a system to come together. Right. So be it sensor technology, but then also be it the, uh, interpretation layer that is able to work with noise and cut that background noise down to almost nothing and find those little needles in the haystack, which was not possible to do before. So, um, I think all of these things, when they come together, what you get is, is quite phenomenal that can become meaningful and longitudinal.

Speaker A: So. And by the way, there's a fascinating interview with Daisy Wolf, she's a VC with Andreessen Horowitz and Andrew Huberman, the famous longevity podcast guy. And Andrew Huberman, before COVID he was not well known. He did not have a big show. And then he began talking about health during COVID And this was a time when millions of people changed their behavior and maybe they worked for. They were working from home instead of from work. And now they could, they had time to listen to a podcast. And so a bunch of them tuned into him and he's credited, along with others, of helping to create this consumer trend toward longevity. And I view longevity as falling under the category of consumer wellness. So longevity is one, along with performance and along with, with thriving and vitality and, and health span and fertility span and others, there's longevity under consumer wellness. And he is credited with sort of reinventing it and with creating this sustained consumer interest in longevity. And he, he said something similar to what you were saying, which was you, you said, and I loved hearing this, that Covid normalized testing yourself at home. And Andrew Huberman, who was this, this up and coming podcaster, but not yet a big podcaster in the 2020 timeframe, he said that he noticed a change when people, when there was this dire health threat. We didn't understand Covid. We didn't know whether it was going to be very serious or just moderately serious or what. And he noticed that that was a juncture when millions of people, he thought, decided to take more control of their own health care more generally. And so, and there's a, there's a, there's a saying or a heuristic or a bad joke in healthcare that from 10 years ago that says that consumers won't pay for their own health care. They want to use a health plan card, which is like a credit card that they bill. Someone else pays for it, their employer pays for it, the government pays for it, somebody else pays for it. That's an old heuristic in healthcare. And what he's observing and what you're observing and today people don't believe that as much. And today we see, you know, who are the three companies that are about to ipo. You know, there's Aura and Whoop and Strava and they're, they're, they're, that's consumers paying for health and wellness. And, and so, and so I think he's pointing to this, this change point which has led to this durable interest in consumer wellness and in, and in longevity, which was during COVID Millions of Americans, you know, decided they were going to take more charge of their healthcare and they did want to either seek extra protection from COVID or high performance or improved wellness or other things like that. So I think that's a very, very interesting observer finding a point in time when things changed. Because I think if you were to look out into the CEO community and the investor community, more than half would still say that consumers don't want to pay for their own healthcare. And so what we're saying is that that's changed. Okay, when did it change? Well, it changed in 2020 and 2020. That's when it changed, or at least according to Andrew Huber. So, so really interesting and well, I

Speaker B: mean, data point, right from 2024, sorry, one data point we do have from 2024 is half a trillion dollars spending in out of pocket consumer health in the United States. So it's real.

Speaker A: Ah, so there's an interesting. If I look at the workflow of a patient getting a lab test with a physician versus a patient getting a lab test at home. So with a physician, you may go in and talk to a physician and they may write an order for a test and then you leave their office and then you, you go get the test. Maybe it's a blood test in a lab and then maybe you have a follow up appointment that could be in person, that could be by phone and then the physician will look at the results and tell you that, you know, this is good or something like that. And then most people, the physician is probably spending less than five minutes on this particular test. They may tell you some things like you need to manage your diabetes a little better. And most people forget that within a day. They have now forgotten that. And they may not have access to the actual test. They may not have a paper copy of the test, for example, but in a home use case, not only do you get the results right, you know, unless, uh, you're mailing it in somewhere, you get the, you get the test right away, immediately, or perhaps through a portal later, but you also have, if you're using it through a modern service, you also have an AI health coach as well. And so this health coach has dealt with other patients who have the same situation. And it has some validated content that it's going to share with you, help you in the area of diabetes. Having a coach, a human coach, or an AI automated coach can be extremely helpful in managing the condition well. And so can you tell us about AI health coaches? This is new to most people. I think most people are not familiar with it. And how about in the world of hormones, what, what, you know, are there, are there these health coaches and what benefits could they offer? And it's such a different way to engage with it than it is to have your doctor spend less than five minutes on it and then to forget it by the next day.

Speaker B: So, yeah, I mean, it is a, uh, very exciting trend with AI coaches. You know, AI is really good at, like, here's how we should think about it. AI is really good at synthesis, right? So it will organize your medical history, it will, um, sort of translate that information. It will connect the dots across, you know, your records and maybe even formulate the questions you should be asking to your clinicians the next time you go in, right? So these are some really cool things it can do for you. It can, as it makes sense of your data day to day, could coach you in, in, you know, sort of again, spot trends that you might be doing in your lifestyle from your physiological data, like sleep in hrv and then go back and look at your health visit, you know, your physician visits in your health records, data, lab data, and spot something that you would never have spotted otherwise, right? So, so I think AI does really well with change detection, pattern finding, personalizing your baseline as opposed to, you know, comparing to a general population, uh, and summarizing things in a way that is actionable. But I. Let me just draw this distinction, right? Uh, which is a very important one. All of this is great, but it must be built on a sound and Trustworthy, worthy data layer. Right. And so, you know, if you are so, so the best role AI is going to play is pattern finding. What we are still missing is good data, the longitudinal type of data that tells your body's story every day. Um, and ex. In so that the complexities in, in, in the human biology can be explained better. What I would not use an AI coach for and do not never uh, want to use it for autonomously diagnosing, for instance. Right. Or prescribing or inventing certainty when we are a hundred percent sure that biology is pretty ambiguous and you need judgment and exception handling in that, in that particular instance. So I think that I really like the phrase AI is the intelligence layer, not the truth layer. Right. So truth layer will come from trustworthy measurements and clinical evidence. And we have to position our, I guess AI coaches correctly. I love using AI coaches. Gotta make that, make sure that we place them.

Speaker A: Yeah, that's really interesting that AI notices patterns and turns that ah, the noticing of patterns across large bodies of knowledge into intelligence. But then, you know, but it, it's, it's not, you know, it's not necessarily the truth because your data could be different from someone else's data or it could be, it could be looking at averages instead of a particular case or uh, or that sort of thing. So. Very interesting.

Speaker B: Or, or really what, what, what uh, Actually Steve, what is really important to point out is that, you know, there are just a lot of our measurements today do not exist at a longitudinal level. Right. So if you're using AI day to day, I think what we still need to build is those measurements on a day to day basis that are reliable, that follow the fluctuations of the body and the cause and effect. Right. I did something, so something else happened and those sort of, you know, that, that measurement layer layer it, it's properly built. So this is why, you know, and AI is just as good as the data that it uses.

Speaker A: So you've chosen to focus on hormones. So can you tell us what kinds of hormones um, are, you know, are consumers and for that matter clinicians. What are they looking at and what is it possible to test at home? What are the kinds of conditions this is being used to manage or in what ways is this helping people to understand better and possibly optimize better their health?

Speaker B: Yeah, absolutely. So we are specifically looking at, you know, longitudinal trends of sex hormones. Right. So you're talking about, we're talking estradiol, progesterone, testosterone, and these are hormones change all the time. Something that we should intuitively know, right? We talk about, oh, it must be your hormones. Well, yes, it is our hormones. And that's because they fluctuate not just monthly or, you know, weekly, but daily and hourly. And a single snapshot, you know, lab draw can fall anywhere on that fluctuating curve, that waveform that our hormones really are. So what we're trying to capture is first making that sort of hormone data more frequently read through at home tests. And we've chosen, you know, blood sampling as a very, A, uh, very deliberate choice. First capillary blood. Because today in clinical practice, blood hormone levels is the only clinical source of truth that is valid, especially for, you know. So you ask, you know, what does it help with? For instance, one very important use case that you know is, is hormone therapy. And the clinical source of truth to manage hormone therapy is to use blood draws. But again, it is a paradox because a single draw, you know, they don't really use lab results to manage therapy today because it doesn't capture the changing signal in the body. So that is sort of the gap and the problem that we are addressing with our system. And you know, our clinical partners, who are kind of a mix of concierge clinicians as well as traditional ones focused on midlife and longevity, um, are very excited about getting their hands on high frequency data. To be very honest, the protocols have to be built. This is what our clinical partners will start with. They are, you know, using our pilot studies, start building their hypotheses. And how will I use this high frequency data to, to help clients and patients? And that allows. Actually what that does is also build out very, very a solid foundation that is on, you, uh, know, clinical foundation and labeled data for the wearable that we're also building for the consumer's case. Right? So the wearable that samples at a high frequency passively needs to be mapped back to this foundational blood, blood foundation layer. And so that we can make that high frequency data also actionable from the wearable. So, you know, if you are, for instance, experiencing menopause, by the way, the, uh, Endocrine Society of America does data survey and found that one third of women who are over 35 are unsure of their reproductive stage. And this again goes back to the gap that we're talking about in understanding our hormones and the levels that we cannot test frequently. So, you know, so if you are someone in that stage and your hormones are continuously fluctuating, we sh. We with our label data and our intelligence layer should be able to help you see your patterns Understand your baseline, understand your deviations over time and, and make sense of where you're at, which is so empowering if you think about it. Yeah. Yeah.

Speaker A: Thank you. And so, you know, one of the conditions here that people often think of is, is perimenopause. And so can you tell us how, how would a product like this help someone? Someone is, is, is, you know, entering menopause and maybe they're starting to get, you know, can you tell us about the condition of perimenopause? And then also when, what would they, what, what would be some benefit from someone who's able to take tests? And now perhaps also with AI, perhaps with, with an AI coach, perhaps with a, with also talk discussions with a, with a human clinician as well. Can you tell us about what that would look like?

Speaker B: Yeah. So as we know, perimenopause affects half the population, but it's a blind spot. So it's a perfect mismatch in, you know, how our biology changes and how we measure it. So today, uh, first of all, perimeter, what is perimenopause? It is about a five to ten year period of time before you are officially in menopause where you are, you know, you do not have your cycles anymore and you are past your reproductive, uh, year. This is a time in a woman's life where there are enormous changes physically and mentally because your hormonal profile, your reproductive profile is changing. It doesn't happen overnight. It takes years to happen. And so you could see a situation where your estrogen levels are spiked up to a point where they're beyond ovulation levels. And then next hour your estrogen levels are down in toilet. So, you know, and that sort of rapid change in your hormonal profile also causes death that are confusing. You know, there are, uh, officially about, I think, 90 symptoms that are identified and logged that are associated with perimenopause. And anybody who is, you know, has a significant other or you, ah, know a beloved person who's been through the stage would now understand that they've seen some form of it. Right? And so, so these are confusing because the rapid change, you can have hot flashes, you can have night sweats. There are plenty of symptoms today. There is no way of diagnosing perimenopause. Okay? So there is no lab test or a biomarker that you can say, we did it and we know you're in perimenopause. And again, I go back to this is a paradox because why does that not exist? Because hormones change all the time. So at what point do you test to even diagnose or, or assert that someone in perimenopause. So everything is symptom based. And then there are some sort of guidelines. Okay, over 45, blah, blah, blah. Right. So this, this is how we assess perimenopause. But what that also leads to, to be honest, is a lot of women end up in a situation where they don't understand what's going on. They go to their provider and explain it, but it's not officially menopause because it has a very specific definition to be a menopause. And they get sent home saying, it's all right, you know, you, you, you'll be okay. And I think we want to change that. We want to. And, and let me, let me put a caveat there too. It's not because our, you know, our, uh, clinicians are not trying or don't want to help. It's because they do not have the tools, they do not have the mechanism to assess, to test, to monitor these changes that are happening in a woman's body. And so that's what we want to change. We want to bring Quinn into the picture where we pair hormone trajectories with symptoms with cycle context with wearable signals. Right. Which is sleep and HRV and label them as we go with clinician supervised treatment changes, for instance. Right. And what this should lead to over time is a improved, better understanding of this phase of life and better decision making.

Speaker A: Uh, that's great. So you could imagine someone who's managing their hormones in the future. They would both be able to see a need for hormone replacement therapy possibly, and then go see their physician about that and also could see, could see sort of spikes and troughs in hormones around perimenopause. And this could, this could help them take, you know, local, local steps or help them go see a physician to possibly get a treatment as well for that. Is that, is that, is that sort of the. Can you tell us about your, your dream for managing these sorts of things for the future for a, for a patient?

Speaker B: Yeah. So I'd say that for a patient. Patient it would be first, let's, let's, let's not call them patients because technically someone in perimenopause is not sick. Right. We are healthy women going through a normal phase in life. And, you know, what we want to do is have them be better understood, alleviate some of the symptoms associated with that phase. Right. So what, uh, what we see happening is a women are more empowered with data and intelligence around their hormonal changes so they can advocate for themselves. They can help the people around them understand better with proof and data. Uh, right. But also adjust their lifestyle, adjust things in their environment to make it more suited, help them. There's, that's one right state is where women can recognize these patterns early on without guessing and go to their clinician with, you know, evidence and ask for help, help early. And what in the clinicians on the other end are empowered by that data? Right, and empowered by the story that a woman brings into the office as opposed to can you think about the symptoms that you have felt in the last six months? So she, who's already experiencing brain fog and memory issues now is asked to recall her symptoms in the last six months in a clinic, doesn't have to do that anymore. She has her story that she can share and in the form of data and intelligence and insights that the clinician should, you know, would, would find empowering as well. And so when these, uh, you know, this becomes an enabler of this relationship, helps with the hormone therapy, helps with the titration of the hormone therapy, managing the dosage properly. Uh, and then over time just keeping track of everything and making sure that, you know, things are aligned and you know, keeping that, you know, staying ahead of problems, staying ahead and optimizing for health. That's what it really turns into.

Speaker A: Thank you. Really interesting. And so any other thoughts on the revolution in consumer home health for our audience?

Speaker B: I think it's a very exciting time for all of us. It's a once in a generation opportunity and a change that not everybody has a chance to witness. And you know, so we should all be very excited about this change and being part of this revolution. Also, I would definitely put a word of caution which is, you know, we need to be always, always take, especially when it comes to health related matters, take things with skepticism, always ground it in reality. And, and you know, our, our physicians and our clinicians are our partners, make them our partners and you know, AI can never replace that judgment.

Speaker A: That's great. Well, well, thank you. It's really great to hear from you Shilpi, and also to hear about your company, quinlabs. So you've been listening to the Digital Health Investor talk show with your host Steve Wardell. And our thanks to Shilpi Gupta, founder and CEO of Quinn Labs. And we'll be publishing this show as part of the Digital Health Investor Talk podcast. Subscribe on Apple and Spotify and leave us uh, a uh, star rating and a review. The next episode coming up will be on Wednesday, August 26th with our guest sage Kanuja on the new patient journey in the age of AI. So thank you so much, Shilpi, for joining us.

Speaker B: Thank you for having me.