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The Diagnostic Gap in Dentistry | $550m Overjet CEO Dr. Wardah Iman
19 Plays6 days ago
Today’s guest is Wardah Inam, Founder & CEO of Overjet, who began her career with a PhD at MIT and is bringing that research into healthcare to rethink how dental diagnostics are done.
In this episode, we explore why diagnostics in dentistry have historically been inconsistent and how variation across providers impacts both trust and outcomes. We discuss how turning X-rays into measurable data can shift care from subjective interpretation to more standardized decision-making. We also dive into the operational side of healthcare—how documentation, insurance, and administrative workflows shape what care actually gets delivered. Finally, we look ahead at a future where diagnostics are continuous, transparent, and integrated into broader health, rather than something patients only encounter when something goes wrong.
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Transcript
Introduction to Podcast and Guest
00:00:00
Speaker
Welcome to the Healthcare Theory Podcast. I'm your host, Nikhil Reddy, and every week we interview the entrepreneurs and thought leaders behind the future of healthcare care to see what's gone wrong with our system and how we can fix it.
00:00:15
Speaker
Today's guest is Vorda Amman, the co-founder and CEO of Overjet, a company building AI solutions for dentistry. And not only do they work in the back office with the usual insurer and admin needs, but they're trying to use AI to change how dentistry
Amman's Journey from Research to Overjet
00:00:29
Speaker
is done. This research was spun out of Vorda's time at MIT, where she realized that dentistry can be quantified, and that'll help many stakeholders from insurers to patients and providers.
00:00:39
Speaker
And today we discussed this quantification problem where AI belongs in dentistry, along with consolidation in the industry, the economics of dentistry practices, and why Warda's company has raised over $130 million. dollars And this episode is a case for what the future of dentistry might just look like.
00:00:57
Speaker
Hi, Warda. Thank you so much for coming on today and welcome to The Healthcare care Theory. Thank you, Nikol, for having me. Yeah, of course. It's great to have you on. But I want to start off with your background a little bit. You started off with a PhD at MIT, working on AI, advanced autonomous systems. So you don't exactly see a clear path into dentistry, per se.
00:01:16
Speaker
I'd love to hear, working
Challenges in Dental Diagnostics
00:01:17
Speaker
at MIT on AI, what really sparked your interest in dental diagnostics and what really motivated you from moving into the lab and foundational research to eventually building ah ah your own company,
00:01:29
Speaker
Taking a risk on entrepreneurship, a hugely different skill set. For me, getting into dentistry was because of her personal experience, having gone to the dentist and and changing my dentist, where I changed my dentist and got a treatment plan, which was very different than I had received before. And it felt that there was something off. The more I learned about it, the more I realized that There was a need for technology to help patients understand their oral health better so that they can understand what the dentist was recommending. So that's why I started Overjet. And when it comes to research versus industry and doing things or bringing things to life, there was more about my own perspective.
00:02:13
Speaker
fashion i've I've been, ah you know, I'm an engineer first. I've been building stuff and I love to see what I build actually getting used by people. That makes a lot of sense. And I can see why you might want to make that switch. And I mean, getting into dentistry, of course, it seems like the problem you detected was as a patient, seeing how things were happening.
00:02:33
Speaker
So difficult, it's often like a gray area between a hard science and something more experimental. So when you notice that difference, I mean, what does it actually look like? Why dentistry a little bit different?
00:02:44
Speaker
And what do you think makes it vary so much between providers or even between the same patient across different providers? So I think it is ah due to the technology being utilized, which is which are the X-rays. And in order to expose the patients with very little radiation, they end up ah you know having less resolution in general. So there is just a um resolution challenge that exists in order to detect little cavities, et cetera.
AI's Role in Standardizing Dentistry
00:03:15
Speaker
So I think that's one of the reasons. Also, in a dental practice, there's different lighting conditions, there are different kinds of monitors, and there's not that much standardization the way you have in radiology, where people will sit in a dark room you know with these expensive monitors trying to look at disease for ah on radiography here. They're moving rooms with different lighting conditions. And some of these monitors are very old as well. So there's a lot of variation in environment, as well as the the modality of extras with the resolution is is lower as well.
00:03:54
Speaker
Yeah, that makes a lot of sense. And I'm almost curious to know like why wasn't that standardized before. I mean, I can imagine that sure monitors, technology, it's it's very different, and especially in areas like psychiatry and psychology. You don't see any similarities at all, but standardization is the goal. So dentistry, what does that trickle down to? Why do you think that we saw so many discrepancies in terms of how care is delivered?
00:04:17
Speaker
Yes. One thing is, even if you have the same disease, you could have many treatments. So I think there's a you know, it's not that the disease they're they're looking at differently, they might be recommending different treatments ah and that that as a patient, you don't understand it all. Right. So it feels like, hey, everybody's telling me a different thing, you know, but it is more about different treatment of philosophies, different treatments altogether um that that that are there. Of course, there's, you know, just generally like different philosophies around treating patients as well. There's also the patient, yeah you know, how high at risk they are and ways ways of treating patients too. So there's different reasons why there might be different treatment plans. But I think for, um ah but unlike say some other places, in dentistry, there's a lot of data actually collected. You have x-rays, you have, which are preventive usually in nature, right? You're not just doing it once you have some um condition, you're doing it,
00:05:14
Speaker
every year or so you're taking x-rays you're being more preventive um so there so it is much more a lot more clinical data is collected a lot of it is quantitative as well there's pericharting that happens etc uh so it is really ripe for using ai technologies where you can actually see the variation uh and progression of disease happening before even a treatment starts to occur uh but the variation in general is um ah you know in in treatment as well as in the diagnosis.
00:05:45
Speaker
Yeah, and when you're working with DSOs, I mean, this idea of quantification, I can imagine, is very important. And it's good to source original customers. And especially for the patient perspective, you're um you don't want people eyeballing it, right?
Administrative Challenges in Dentistry
00:05:59
Speaker
So i can imagine to what degree was their quantification before Overjet came in with AI tools? Like, what do you think that looked like beforehand?
00:06:07
Speaker
Was their quantification, but they just couldn't act on that resources? Or do you think there just wasn't the data? Where was the initial roadblock before Overjet? Yeah, so I would say when it came to ah ah x-rays and radiology, ah that there there was some more subjective analysis happening. there There was eyeballing for bone measurements, you know, how how how how large is the lesion, et cetera.
00:06:35
Speaker
With overjets, we started to quantify patients. things like bone levels, things things, you know, in terms of whether the, the, the pin caries lesion is, how much is, is it in the enamel, et cetera. So we did bring that quantification as well, but ah where the quantification did exist was, ah again, you can say it's subjective because it's peri-charting, but, you know, you were still writing in numbers and structuring that information. um However, in some practices, they don't really peri-chart effectively, you know, ah they're just writing to, 232 or 323, etc. So um how how much can that be? Actually, you can rely on that data. Of course, that is also changing with, you know, ah we're introducing our of of ambient AI models so that we can they can chart more faster and more effectively. But there's been there's been a lot data being collected in the industry, but not only all of it being utilized.
00:07:34
Speaker
Yeah, and i can imagine it's an issue for the insurance side of things too. I know for a clinician, they want to work with patients, help help them out, and a lot of their time is swept up with insurers, administrative issues, staff issues, and part of that is not just and part of that is not just about like the data. It's hard to build an interoperable process between insurers and clinicians. So was wondering...
00:07:56
Speaker
Building that out, what does that look like? I mean, I realize a lot of people, Dennis said to complain about how maximums have been haven't been changed since the And dental reimbursement is not as strong as medical reimbursement.
Overjet's Growth and FDA Clearance
00:08:06
Speaker
There's all these different issues. But within your conversation with dentists and the DSOs, what were you picking up on when you first looked at this problem?
00:08:14
Speaker
So I think, Nicole, you're absolutely right in terms of the amount of administrative burden that is on the providers to submit the claims, you know, code effectively, collect from the patients as well. And, ah and, If we can do things to improve that, like that's definitely worth it. ah On the payer side as well, they have the same administrative burden because they have to now deal with the claim, deal with the phone calls, submit that information. So there's a lot of back and forth that happens between providers and pairs. And I think that is the opportunity.
00:08:46
Speaker
The opportunity is not to say one wins, one loses. It's like how do both win? And both win by reducing this administrative burden that they have, ah by automating a lot of the things, bringing the information to the to to where it's needed at the right time so that you don't have the phone calls, you don't have the back and forth that's happening currently.
00:09:05
Speaker
Yeah, that's interesting. And hopefully I think a lot of that will come from the data, but I guess a lot of having, you you want to have a lot of different systems talk each other. And soon enough, I do want to get into overgen. And I'm curious that, I mean, one thing a lot of people will think of when they think of dentists is that they think of smaller private practices and just a few people, but it seems like there's been a lot of consolidation in this industry.
00:09:26
Speaker
um And a I feel like it wasn't a very economical solution to just have individual practices at first, of course, according to them. I mean, now you have this consolidation. what do you think changed about the landscape between these DSOs?
00:09:38
Speaker
um What did it look like when you guys first started versus today? don't think. Yeah, the trend had already started. So by the time we got in, um i would say private equity was in full force, have been consolidated practices. i would say a few other trends that played in our favor was also digitization in the industry. Right. So, um you know, before that, and maybe like two decades ago you know, there there was more analog X-rays being utilized, you know, ah not everybody was on digital platforms, et cetera, as well. And then, um
00:10:13
Speaker
Then they started getting on digital platforms, digitizing all this data that helped us ah to to connect with these systems, get the data as well. So not only were there more market challenges, but also digital challenges as well before.
00:10:30
Speaker
Yeah, that makes it difficult. And I imagine that, I mean, now we've kind of set up the problem. There's not only a lack of objectivity in dentistry, but there's also a huge problem of data silos, inability to talk with insurers and and administrative issues.
00:10:44
Speaker
So, i mean, now you're at MIT and you want to go design a company, you call it overjet. But what were the kind of first principles and constraints that guided you through? How you thought about the product at first hand, of course, you could have done something just like a clinical decision support tool or something on the admin side. It seems like now you guys are doing a bit of both. But when you first thought about this product, what were you guys thinking of and what was the overall goal and underlying principles that um you guys ended up designing the product around?
00:11:09
Speaker
Absolutely. So when we started this very early on in terms of what these deep learning models could do at that time. And ah so first it was, was kind of based on that. We went with the first application that we went with was actually bone bone loss of bone levels um and one of the reasons was that uh um we said it it's just a measurement it's not really you know up a pathology and unless you know the measurement after a certain threshold you know things can get worse but we we approached it in such a way which allowed us to get fda clearance very easily um because uh there weren't that many clearances before that that um i think that that
00:11:48
Speaker
you know, that that helped. Also, I think just dealing with, you know, what the accuracy could be, we were doing a few things, ah so or we started doing things a certain way. um You know, if you've if you think through why, you know, we put pole levels a certain way, why is there dots on on where the the CJ point is, et cetera. It was like, we were not quite close enough. So the the dots were bigger. Now they get got smaller, right? Like, so we we were dealing with,
00:12:17
Speaker
where where the majority of the technology was. But the good thing is very quickly it started to surpass dentist level accuracy. And I think that's when things started to change where it's like, okay, now this can provide a lot more value to the dentist. And and dental practices and what do we do with that? And for us, it was always worth tying ROI, tying it to things that people could actually measure as well, rather than just saying, hey, it's helping you do your job much more effectively, especially when you come you think about DSOs. The buyer of the product is not the user of the product. So even the user might love it. The buyer is saying, hey, what's the value? of Prove it in numbers, et cetera. So those were like some of the challenges that we have to had to build for and and and and sort or or solve.
00:13:07
Speaker
Yeah, it's definitely a really interesting problem in hospitals, dentists, anywhere working with clinicians. The buyer is someone who wants to look at ah ROI. The clinicians want to see something that works, right? So there's a bit of a chicken or the egg problem or a dilemma there. And you want to prove something works, but you have to first
Commercialization and Product Overview
00:13:23
Speaker
prove the ROI, which is difficult. So it creates a bit of an issue.
00:13:27
Speaker
i was wondering, did you guys start, first of all, with having the bone detection issue? Did you start with cavities, tartar, move on to that first? Yeah. Or did you first sell them to DSOs when you had the initial software? At what point did you guys start to commercialize versus build the technology?
00:13:41
Speaker
ah We we ah were building technology till we got our first FDA clearance and then we started commercializing it. So that's like 2021. Awesome. Yeah. And when you were building that out with the force working with the FDA and clearance in there, was it very similar in terms of building other technologies for you? mean, of course, you had um gum disease cavities. Was it a similar methodology between them?
00:14:06
Speaker
No, so it was very similar methodology and also after some time we started building building ah ah these like foundational models which would learn from all the x-rays and understand um what x-rays really were about and then fine tune it for the different ah pathologies that we were looking at. and we we I would say, you know, ah very quickly we started to add more ah capabilities. But even though when we were building and we couldn't sell it, we were testing in practices. So we were, you know, under an IRB, we were in practices testing them out, seeing how they perform and and and taking that feedback and improving the models.
00:14:48
Speaker
Okay, because of course that would be necessary for FDA clearance, which would help demonstrate i mean that idea of r ROI. So what did it look like dealing with the FDA? I can imagine we're seeing more and more that the FDA has clearer and more stricter regulations, which is probably good at the end of the day.
00:15:02
Speaker
um in terms of that, clinical decision support software and 510k clearance, like was it between those things? Or what did the process look like in those early days, especially given how the FDA has changed so far since then?
00:15:14
Speaker
Yeah, so I think you're absolutely right about it. It was looked very different. There were these different guidelines that were created. um You know, there were like drafts at that were there that were still being, you know, ah opinions were still being provided on them. um Also.
00:15:34
Speaker
When we started, ah Gary's detection was class three, and then it became a class two device. So it got downcoded, which was like a big deal. really helped us get this done sooner. Although it did take a long time, but it still would have taken longer if it was still a class three device. so it and um And then, of course, there were some players who had already gone, not in dental, but in other areas. So we could learn from them as well.
00:16:02
Speaker
That's interesting. And what lessons did you learn from that? I mean, of course, handling the FDA, this whole process, was it more of just like a back and forth between them? i mean, I know the FDA can be interesting to deal with, or was it more just building that rigor in terms of having a great data set and a great IRB? What was the main mechanism there?
00:16:19
Speaker
Yeah, so I would say ah at that time, you didn't know how to get cleared, right? so ah So we were reading other people's submissions and trying to understand those and of course talking with them and trying to understand that as well. So I got in touch with folks who had gone on clearances before to help us out with you know what we had to do. We did not know what we will have to submit. So how did you do a pre-sub to the FDA to provide us that information or or help tell us that we were in the going in the right direction as well. um And then being able to ah do the clinical studies with with the information. So I think there was a lot of learning, but i you know we were doing it little slowly because we didn't know exactly like how to do it as well. And for example, we couldn't find anything that was like a segmentation having been cleared, right? It was all detection, which is, ah so the the not ah ah the
00:17:16
Speaker
um with the the, even the ah measurements that we were utilizing to look at the accuracy, et cetera, you know, we had to kind of figure out what the endpoints would be for these studies. What would that look like? There wasn't somebody who could just say, hey,
00:17:32
Speaker
Let's just look at that and just copy this. Yeah, I mean, there was no overjet 1.0. You could easily, I mean, at least with clinical trials, there have been decades, and I'm sure each trial is different, but there's some precedent to go off of.
00:17:45
Speaker
So I can imagine the early days of clinical support in AI must have been pretty different. So now that we've set the ground layer, you guys have gotten in the approval of the FDA and I'm commercializing. And what do the how would you describe the product today and what do you guys offer?
00:17:57
Speaker
What is their value prop and what are the different services that help DSOs and individual clinicians just do their job better and build a better business? um What does Overjet enable people to do?
00:18:09
Speaker
so now we have multiple products in the market uh we have our you know core ai which is our vision ai looking at x-rays uh and looking at past charts understanding what's been uh treatment plan what hasn't been treatment plan and being able to then highlight the areas that might need some review but more importantly why dentists like to use the product is to communicate with patients more effectively so Dendists know how to read X-rays. They understand what radiolucencies are, what radiopacities are, etc. But patients just don't. They're seeing gray, white, and black. And dentists can use the technology to and use the visualizations to help communicate with patients effectively when they're talking about... um
00:18:55
Speaker
a cavity, it's red, it's highlighted when they're talking about ah bone loss, they're seeing bone levels increase over time, cetera. And then as patients understand it, they're they're not hesitating in doing the treatments that they, that if they don't understand and and are more convinced that they should get it because they can see it for themselves.
00:19:19
Speaker
The other, you know, beyond that, we also built a, in on the clinical side, and technology to ah what's called ambient AI listening, being able to transcribe ah what the dentist is saying so that they don't have to spend time with the keyboard writing these notes. They can just say it out loud. Similarly with period charting, um you know, the hygienist is...
Improving Patient Trust with AI Solutions
00:19:45
Speaker
ah while they're pericharting they're trying to put that information into the uh into the computer as well and being able to uh collect that through voice rather than them typing it in um and and and then utilizing that data further for insights and and ensuring that claims are uh submitted uh more clear our cleaner claims are submitted uh because the right information is present
00:20:09
Speaker
And then ah third, last there would be around our insurance verification ah where using automation to help um practices get eligibility and benefits information comprehensively across 300 plus pairs so that they're not again looking at different portals calling the providers and they can actually ah bring value to the a or or ah without spending a lot of time they can provide the value that that they're doing with otherwise.
00:20:44
Speaker
Of course, yeah, and I can just imagine just being a patient, I feel like dentistry is one of the areas where trust is maybe a little bit weaker because you're going in, you often want a second opinion, maybe you'll just think about it.
00:20:56
Speaker
And it's honestly hard because it feels so subjective and it's you can't really get a good picture on it. I mean, just having a color, a clear chart already makes things feel so much more scientific and accountable and I guess basically credible, really.
00:21:10
Speaker
So assuming the idea is that you have case approvals increasing with this technology, but could you just speak to that whole subjectivity part of dentistry? i mean, how do you see that planning out? Yeah, so I think, as you said, for patients, it there is a belief that in the general public that if something, if your tooth isn't hurting, there's nothing wrong. And when you have a cavity, it hurts. That's not the truth, as we know. like you know The cavity has to be like so very big for the for the ah for the tooth to start hurting. And so I think that is the misconception that ends up providing a lot of doubts ah for patients. And um and that's what they expect to happen. And I think that's something that like we need to
00:21:57
Speaker
educate the patients just in general as well. you know I think schools can do a better job at you know ah um or oral health education. But in terms of the software, what it allows providers to do is better communicate with the patients. And we've seen, um you know, ah we have a lot of cases where it's like one of cases where the patients are like, well, like this is like I, i my dentist said it, I you know didn't believe it. And then once I saw it, you know, like I had to get it done. Right. Or like you can see even like now, even um
00:22:31
Speaker
in ah Yelp reviews or Google reviews, you see people patients mentioning, you know, seeing the technology and how cool it was. And like now they finally got the treatment that they needed, etc. So that is that is, I think, you know, ah the stories are definitely did there. And then in terms of the data as well, we've we've seen, you know, 20 plus percent increase in case acceptance. And I would say there's even potential, like the highest we've seen is like, it's like 75%. And maybe there's more, but you know, ah no, I think there is actually more as well. ah But in general, I would say it does increase and, but every practice is different. um
00:23:12
Speaker
And of course it doesn't increase magically. You have to open it up in front of the patient and show the patient that information as well. So I think it's, it you know, depending on the adoption, et cetera, it can be ah um you know, the the increase can be different, but it is definitely attributed to case acceptance increase.
00:23:33
Speaker
Yeah, that's really interesting. I mean, I trust my dentist and I'd still be, but it's still be great to get those graphics when I go there. um My dentist was a private practice. And I think about a lot of the work that you guys had to do was probably going to market it with DSOs.
00:23:46
Speaker
And I know it's a little bit different from the way private practices work. So I'd love to hear the intuition behind that. um Principally, generally, like selling to these smaller practices is probably easier, but of course, you have to do that many more times to make the economics work out long term. So what was the decision between going almost enterprise first versus individual practices?
00:24:05
Speaker
And what did that market look like initially when you're looking at these larger PE-backed, maybe somewhat bureaucratic organizations versus clinicians when you're selling directly to the end user at these private practices?
Innovations in Dental Processes
00:24:16
Speaker
What was the trade-off there? Yeah, so we were... um We started off DSOs, but i would say we were selling just well the first few practices that we sold were smaller practices anyways. And so we were always, we didn't have a big focus on it.
00:24:30
Speaker
um But, you know, there because of the just the ratio of small practices to ah ah to DSOs, we had always like, and even inbound coming that du that we were serving. So we've been serving them to practice from from from day day one.
00:24:45
Speaker
ah About one and a half year ago, we really doubled down on smaller practices as well. And it's one of our biggest growth levers right now. So I would say the the cool thing about the product is that a one-off practice can get it and a practice like 500 plus, ah you know, or DSO with 500 plus practices can get as well. And this is the same product. The only difference is at the managerial level, you have another like view that you can look at multiple practices so you can aggregate the data, etc.
00:25:15
Speaker
So we have a DSO analytics analytics dashboard that we provide. But the product itself is exactly the same that gets used in a small practice and or a large DSO. And I would say when it comes to trainings, it's even even easier for us with small practices because they're the ones who bought it. right It's not like, so say, an organization bought and now you're getting something. like They went on online, they bought it they're excited about it. And and the question is, how do you bring the same excitement to TSO so clinics who didn't actually buy it? they were given It was given to them. And so, i you know, Both have their pros and cons, but you know ah DSOs are easier definitely sell to. You're selling to one group and they can roll it out and into hundreds of practices. Easier to serve in that way. um
00:26:01
Speaker
IT t is much easier, et cetera. But with smaller practices, you know the passion is there and they're buying it and there are a lot of them as well. Yeah, I think with a lot of the strategy behind most of the private equity involvement is that, at least with the dentist, the individual private practice is a little bit more autonomous, but I can still imagine if the private equity firm tells you to download a software, you might be a little bit skeptical at first, but hopefully after using it, if the product is good, you see that retention.
00:26:31
Speaker
So with Ambien AI, I mean, where does that come into play? I know you're on the clinical side. side We've talked about that a lot, but a huge part of the work that you guys are doing is on the administrative side. So dealing with insurers, getting billing and admin set up, what does the and ambient AI do that actually helps facilitate a better discussion and revenue cycle process?
00:26:50
Speaker
How have you roped that in and built those rails together? So on the ambient AI one thing about dentists is that they're very, very busy, right? And there's a lot of of information that is being transferred as well. So like, especially when the patient and the dentists are talking, the documentation that needs to happen at that time, even when they do procedures, right, they need to document it. And a lot of times,
00:27:16
Speaker
ah That it happens after the fact, you know, like once the patients are gone, you know, in in their free time, that's when they're documenting. So first is like the easy bit, like how do you actually document it during the the same time that they're doing the ah procedure so they can view it, make sure it's correct, et cetera, rather than after the fact that they might be missing some facts and all that stuff that happens. That that ensures that the the notes are complete and and and when the claims are getting formed as well, the right information is is there.
00:27:44
Speaker
Then second is, you know, as I mentioned earlier on the hygienist side, again, the hygienists are pericharting. While they're pericharting, they're supposed to put like numbers into the ah on the computer as well. And sometimes they have another system doing it. Sometimes they're doing it themselves. So by having um using voice for ah noting down this information also helps to reduce the time. So we can actually the hygienist can do, for example, four minutes to do the perichart rather than 10 minutes plus it will take them without it while while they're putting in all this information um as well so that that that so they can have much more thoroughness in their exams uh and not just like put three to three into into this as well uh so that that really helps
00:28:29
Speaker
Yeah, i think i've but of course, I think I've read a little bit about this on your website. It seems like the overall goal, which I'm really excited about, is that you can have an instantaneous pain approval while the patient's still sitting in the chair just getting it out.
00:28:41
Speaker
And it's a lot quicker than what we see today where it's weeks or days of just back and forth with insurers. So what are main barriers there to get a patient who comes in for a procedure? And we have the data on what they are previously and you want to get something approved instantaneously.
00:28:56
Speaker
What are the barriers for us doing that today? Is it just these prior auth wars, the AI, is it something different or what are the roadblocks there? I would say um right now, it' just getting the bears onboarded. So we have some major bears onboarded onto this platform, which allows you to get a decision in real time from the bear. Think of it as giving you a pretreatment estimate ah where um it's not you know where you can provide a ah more effectively to the patient um and uh and not you know they don't get a surprise bill later on um that that itself
00:29:33
Speaker
I would say it's like a win for the patient, it's win for the payer, it's win for the provider. So it's one of those things that that is going to happen. And we're in a very good position to make it happen as well. So we are you know rolling this out across different practices and and across different dental groups or dental ins insurance companies as well. And we have some major insurance companies already on the platform that are providing these decisions in real time.
00:30:01
Speaker
Yeah, and when you want to build that win like guess win-win-win scenario with a patient provider and the payer too, think something that's interesting is that you have to have the right incentives for all three. um Because a win for each is not always a a win for all.
00:30:16
Speaker
so I mean, ah how do you align incentives there when the patient just wants to pay as little as possible? The provider just wants to deliver care and hopefully ah come up with some pay for themselves. And the payer just wants the data and wants to get some sort of cost control so they can improve their bottom line to some extent. Everyone at the end of the day has their own bottom line.
00:30:34
Speaker
How do you interpret change these incentives? They all fit together within your platform. I would argue that everybody in this, so payer, provider, and patient want to get the or do the right treatment at the right time and have no administrative overhead to do that. So like that, that I can, you know, like we can all agree at least on on that premise. Now, you know, so because the thing is if you say, you know, the pair,
00:31:03
Speaker
doesn't want you to delay your treatment and now get crown. You know, if it had to be a filling, it's not in their best interest that you delay your treatment and then get a crown um ah because, you know, they want to pay the the least amount ah as well.
00:31:19
Speaker
ah Patients, you know, it's not in their interest to not do the filling and get a crown as well. ah You can say oh maybe some provider might be interested, but again, like no, then no,
00:31:31
Speaker
person went to become a dentist to just do extra care, extra treatments, they went to treat patients, right? So like by, by just, you know, the fact that they've dedicated their life to this mission is, you know, one of the things that they're also tied towards providing, doing the right treatments. Of course, they don't want to You know, but and and but the thing is, they should not be doing a filling when the patient needs a crown because the filling will fall off. Right. So it's it's one of those things that doing the right treatment at at the right time is like good for everyone, like not spending a lot on ah the overhead. um And in that way, patients have to pay less. Right. They don't have because in the end, who pays the patient pays, right? Like the the premiums they're paying for, whether it's coming out of their paychecks or whether they it's coming out of, ah you know, they're paying for it individually, et cetera. But somebody, you know, they're they're really paying or the employer is paying for them.
00:32:21
Speaker
up And then it is, you know, of course, providers want to get get paid for the work they do and peers don't want to pay more than they need to as well. so I think there is good alignment. I think it's just the fact that um in the past, when you had to put bodies to you know people to do these tasks, the only way you could do it faster was to add more people that would increase the cost. The only way that you could actually ah have a thorough review was by having more people that would increase the cost. So there was this trade off.
00:32:52
Speaker
And I think now with technology, you don't have to have that trade off. Right. You can have technology where you're analyzing all this data in real time, getting this information cheaper than you would you were doing it with people, even if they were taking weeks to review it. So there is an opportunity to really reduce costs for everyone.
00:33:08
Speaker
Ensure the right treatments are happening. You're not doing subjectively. You're doing it objectively, doing it at the right time. So you're not getting, you know, at the major treatments that are causing more pain for the patient, that's causing more money for the ah for for
Future of AI and Robotics in Dentistry
00:33:19
Speaker
the pair.
00:33:19
Speaker
and And then you know what we're really trying to do is then measure that oral health score and then improve their old score, ah score health score over time. So but how how do you actually start to measure what is a patient's oral health condition and how is it improving over time or or getting worse over time and and being able to measure that as well?
00:33:39
Speaker
Yeah, think there's a lot of interesting points to unpack here. It's very interesting that in the long run, everyone's incentive is the same, but you want to make sure there's some level transparency and also just information to ensure that the right care is being delivered and things like that.
00:33:53
Speaker
you can imagine it before, yeah, it would be difficult because we just had need more staff to get things correct. So then... makes it more expensive for the payer and the provider and then more expensive for the patient. And you see this kind of cycle getting on here. So I can imagine, again, that Overjet or AI can really orchestrate these set of incentives.
00:34:10
Speaker
So I'd be curious to know, like within the next few years, Overjet's already accomplished a lot. Where do you see this going in the next three to five years? um We already have large language models. People know about GPTs, but within Overjet, what are you what you excited about in the next three to five years?
00:34:26
Speaker
I'd first, like just quantifying this more, you know, quantifying oral health, being able to measure the outcomes and values that that are actually delivered. And I think that will really align around what treatments need to happen so that, you know, you're getting the most amount of value with the treatments and and actually measure, you know, the health as well. Right now we do get treatments without, ah you know, without knowing how it's impacting our oral health. So I'm excited about that. The second thing I'm excited about is the the connection between oral health and overall health. you know we We know that yeah the ah ah that there's
00:35:04
Speaker
you know with periodontal disease, you have increased ah ah ah risk around diabetes, cardiovascular conditions, et cetera. and But really are starting to improve those, not just talking about them, not showing in research, but showing how we can have um ah ah and be able to help patients ah manage their oral health better because of how they they're managing their sorry overall health better, how they're managing their oral health and and and vice versa. I think there's like a lot of interesting stuff to be done here. And I think there will be a lot of work that happens there as well. And then I would say, you know, because you did go all the way to like five years or so, I am interested in how like robotics plays out here in the area, in this space. And I think there's there's going to be a lot in robotics for dentistry too.
00:35:56
Speaker
Yeah, no, that's great. And I think that um I also have one last question. I mean, you had your PhD in the area of urban robotics, electrical engineering. Where do you view that in the role of healthcare care coming in broadly? I know with dentistry, there's a few applications, but do you think there's more room for involvement of robotics and electronics to that level? I mean, what have you seen just outside of dentistry that you're maybe also excited about?
00:36:19
Speaker
i've i've seen some interesting stuff around um the cleanings right there's a lot of mechanical work that happens in dentistry so the interesting thing is all the mechanical work can be uh done through robots uh uh but you know the question is will it be fully autonomous will it be like you know ah will it be guided i think that that will depend on how quickly the technology adopts and how it works with the technology as well but i think you know uh um ah from cleanings to treatments, et cetera. There is a big role of robotics in dentistry.
00:36:52
Speaker
Yeah, no, i think that's going to be very exciting. And I'm honestly really curious to see where things go, um not only with robotics, but with AI as as a whole. And I think that this is an industry where it's just like maybe a decent bit of friction, but it seems to be changing pretty quickly. So really excited to see where things are going over, Jay, and really appreciative of your time today.
00:37:12
Speaker
um but so thank you again, Warda, for coming on. Really appreciate it and hope to have you on soon. Thank you, Nicole. Thanks for listening to The Healthcare Theory.
00:37:24
Speaker
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00:37:38
Speaker
And if you really want to learn more about what's gone wrong with healthcare care and how you can help, check out our blog at thehealthcaretheory.org. Repeat, thehealthcaretheory.org. Again, i appreciate you tuning in and I hope to see you again soon.