Episode Transcript
[00:00:05] Speaker A: Hi, I'm Eric Whedon, a seasoned go to market software executive, team builder, and a catalyst for revenue growth. My journey is fueled by a profound passion for ushering in a new era of dental healthcare through cutting edge AI technology.
[00:00:18] Speaker B: Hi, I'm Liz Strickwarda. Since 2008, I've been immersed in the world of dental marketing and business technology. At Truelark, I focus on explaining how generative AI can help multipractice dental organizations scale while redefining the patient experience. Together, we welcome you to navigating DSO innovation, where we have unfiltered conversations with dental leaders and discuss how they are using tech to win in this booming market.
[00:00:51] Speaker A: Hello, everyone. Today on the show, we have Rajiv Bhatia, the founder and CEO of BPK Tech. BPK Tech specializes in delivering tailored software development and business solutions. Originally from India, Rajiv now calls the USA home. In addition to his work at BPA, Rajiv is also deeply committed to making a positive impact through his involvement with the PKB Foundation, a nonprofit dedicated to helping those in need need. Welcome to the show, Rajiv. We are delighted to have you.
[00:01:20] Speaker C: Thank you, Eric. Thank you, Liz. Good to be here.
[00:01:23] Speaker D: Awesome. Okay, well, let's get started, unless you want to add anything to that intro. We just. Dental has traditionally not been the easiest market to break into for tech companies. So first off, just kind of let us know what it's been like building a software business in the dental DSO industry.
[00:01:44] Speaker C: Yeah, tell me about it. I lived with one. My wife is a dentist. And, you know, like husband wife, there are challenging times. But having a wife who actually is a dentist, challenges are quadrupled. And she hired me as a free resource to help her with her it. And then after I couldn't give her enough time, she fired me. And I'm like, how can you fire me? I'm actually free. So that's how it goes, right? So, you know, this is my fourth startup, and before this, I had another startup in dental, which I exited. And it's interesting how the dentistry has come a long way from where it was when my wife started her business. And a lot of innovation is happening. There's still a lot more to be done, but where things are today, you know, of course there's a lot more we can do, but we've come a long way.
[00:02:42] Speaker A: Rajiv, on that note on the innovation, where have you seen the most progress made since the time that your wife first got started to where we are today?
[00:02:52] Speaker C: I mean, if you look at just the basic things like communication software, there was only one choice. I think it was like Lighthouse 360. And look at now, I mean like no one is building any communication software. It has become commodity, especially patient relationship management, right?
Back in those days, no one knew about AI. Now AI is everywhere. It's solving a lot of problems, right? You look at robotic process automation, where all the menial tasks that staff does today, all the grunt work that's being automated, you know, you look at revenue cycle management, it's like it was just nonexistent back in those days, and now things are much more different.
[00:03:34] Speaker A: Right?
[00:03:35] Speaker D: So I understand one of your first solutions or tools had to do with insurance verification, was that correct?
[00:03:43] Speaker C: Yes.
So there is quite a bit of problems that are still not yet solved in dentistry. One is data, which is, you know, you own the, you own the practice and you are, you have a practice management system that you work with and yet it's hard to get the data out. And so in order to do anything, you need to know what your data looks like. And that was a common problem that every dental office, and especially pe founded DSO companies face. And so our, so when I started looking into this, I was like, how can we make it easy for dental offices? And so the first product that we launched was to actually get access to data, to really understand what that data looks like. And then after that we started to work with a lot of dental ecosystem companies. And by that, what I mean is providers to dental suppliers, to manufacturers, to dental software companies that supply software to dental offices and then to provider to payers. And we started to see a common theme. Number one, data is just hiding away in practice management systems. So we had to get access to the data. Once we got access to the data, we're like, what are the main common problems that everyone is trying to solve but no one has solved? And revenue cycle was one area where we felt that there was an urgent need that a problem had to be solved. And that's where we went in.
[00:05:20] Speaker A: Rajiv, is it pretty easy to get dsos on board with new technology, for example, related to revenue cycle management? Or is there a large educational process to get buy in, to get dsos that are quite frankly maybe not used to all this new technology? Maybe they aren't even aware of what's available. What does that process look like for you?
[00:05:44] Speaker C: I think it all, I'm gonna say, hate to say it, but it depends. I think some, some of the dsos that are trying to build a platform and they want to make everything automated as much as possible so that their labor costs are significantly reduced, not because they don't want to hire labor, but because they just can't find labor. Right. And so, so automation is the key. And how far can you go with the automation? Right. I mean, if you just look at within revenue cycle operations, there are different ways how you look at revenue cycle. A lot of people call it very broad. I mean, for us, from our product perspective, from our software solutions perspective, we call it insurance verification, claim management, and then payment posting. And so from an insurance management insurance verification perspective, that's probably the grunt work, the most grunt work because, you know, you look at the data that you get from clearing houses through payer portals or, you know, you get no data from both those places and then you have to call insurance companies. So it's just really frustrating and it's fragmented. And that's where we wanted to go in there and solve that problem to see how easy can we make it so that customers don't have to lift a finger. Well, they do, but reduce the overall workload so then they can get most amount of data so that they can make a better decision on what their claim would look like.
[00:07:06] Speaker D: So as I'm listening to you, I'm thinking of different areas where they get ROI. You know, it helps ease their hiring challenges because that's, that's been bad for years, even then, since COVID even harder to hire and then just the gains in efficiencies. But what are some other areas where they get a return that maybe are not so obvious?
[00:07:32] Speaker C: So there are a lot of different places. For example, if you look at a DSO that has a practice that has like ten different practice management systems, trying to get data from those ten practice management systems to make it look like, you know, patient data from one practice management system and patient data from another practice management system, you are speaking, you know, French and Spanish and then trying to convert that into English, right? It's an upheaval task and that's where we come in where we help a lot of dsos build up their data solutions because they are trying to get the data and then married with their labor report from their ERP system or their payroll system, and then trying to figure out what is my productivity in my office. And it's really hard.
If you look at other industries, it's much simpler. And why is it harder here? Because of access to data. And it's just complicated. And a lot of dsos, they don't want to invest or they don't understand the value of investment. And that's where it becomes our job to actually show them the ROI in terms of look, once you're able to measure what really matters, you will be able to make matter decisions. Now you're making decisions based on gut feeling and you know what happens when you make a decision on gut feeling, right?
[00:08:56] Speaker A: So with that being said, Rajiv, looking at the DSO landscape, would you say, you know, ten or 25% are on target, you know, with, with being able to take advantage of data, or is it less than that? Where are we at in the curve of kind of adoption and utility?
[00:09:17] Speaker C: So last year we did a survey. We called in around 330 dsos and the goal was to break it up into three buckets. One was anywhere from one to 25 clinic, the second was from 25 to 100 clinic, and then the third bucket was 100 to 500 clinic.
Anything above 500, we didn't call because they don't respond to us or second, they just have their own large tech teams. And these are the guys, these are the guys and girls that are trying to understand, you know, what they have. And we identified that over 30% of them, they really understand the value of data and they are doing something about it.
We were surprised that 70% of those dsos, they didn't understand that there were solutions out there or they were providers like us who can come in and solve problems. And for them we felt that this would be a good challenge for us to go and train them, teach them, educate them to say, look, there are options available. Either you can buy a software or you can hire companies like us to actually build a software. And so when we start to go out there and when they start to see the value, they're like, oh, I understand what my productivity costs are, what my supply costs are, what my lab costs are. And then, you know, they start to look at where are the avenues to either improve efficiencies, reduce cost, or a combo of both.
[00:10:47] Speaker D: So I would imagine that, you know, it takes quite a bit or at least some bit of customization for each of your clients.
Can you give any examples of kind of some more unique problems, different, you know, multi practice groups or DSO have that where you were able to customize your software for their particular process?
[00:11:13] Speaker C: Yeah. So let me just step back.
We have two companies. BPK tech is a company that builds tailor made software and that's where we do a lot of different projects. And then we have another brand called dentistry automation that sells API products as well as revenue cycle products to customers, mostly to dsos. The first company, BPK Tech, which is where we are building tailor made software, we do all kinds of projects. For instance, recently we finished a project about doctor compensation system in there. We built something very unique where if you ever work for a DSO, when you are hired to a DSO, if you're a fresh grad, you get put on a different pay package. And then once you get experienced, then you go into a percentage based off of the revenue that you bring in. And then there are other rules and regulations if you become a partner. So there is a lot of different combos. So we built that doctor compensation system for a DSO and it was so complicated because every acquisition that the DSO would do had a different and will have different rules, right? Recently we worked on another project called Hygiene Powerball. And the idea was that if the overall revenue of the clinic, because of hygiene and doctors involvement and scheduling involvement, if it hits a certain number, each of the staff member will get a certain incentive. So again, those are somewhat related. We recently worked on a data warehouse project where we built a very large data warehouse for DSO where they could pull data from ERP system like Sage or intact or Netsuite, or from ADP or from payroll provider like Paychex. And we were able to pull the data from their practice management system from all of these places, Google as well as some other marketing systems, and build a solution through which they could actually have an insight into what's going on inside their data.
So just wide variety of things that we do not only in here, but also in AI and machine learning, where we're seeing that a lot of these gestures are investing a lot of time and money into building a platform, building solutions, because they know that it's going to have a much greater ROI for them and they would need less resources to do what they want to do.
[00:13:43] Speaker A: Rajiv, do you see dsos largely understanding what they want to build and coming to you for that expertise to execute? Or are they really relying on you to provide that industry insight to understand what the opportunities are, where they might have a general concept of like, hey, we need more access to and better intelligence around our data.
You know, maybe, maybe we need a little bit more direction in terms of the possibilities and imagination of what can be done.
[00:14:13] Speaker C: I think it's a mixed bag, I would say. There are a lot of. I just talked to a DSO and they're owned by a PE firm that has probably three dsos in their kitty and they are very tech savvy, they know exactly what they want and so we are helping them, you know, solve some of their problems. And then I also talked to another DSO that has no technology member on their staff. And so we advise them to actually hire a technology member or use our services or our partner services to look at the lay of the land within their DSO and then understand what are some of the challenges that they're facing. Because we are a vendor for them, but we want to be a partner with them. That's our approach. So we lend a free hand to say, look, we have analyzed. So. So for example, if you were to start on engaging with the DSO, we would probably generally do a discovery and the discoveries, you know, probably two to four weeks, depending upon the DSO owners or the investors, they will tell us about what are some of the challenges that they are trying to solve. It's almost like we build a laundry list of wish list items that they want to get done, and then we will go back to the drawing board and say, you told us these 50 things. Out of those 50 things, we feel that these ten are going to give you the biggest ROI. And out of those ten, because you have whatever budget, you should focus on these three.
And that approach has helped significantly.
[00:15:44] Speaker D: So they have a roadmap.
[00:15:46] Speaker C: So we create a roadmap and a strategy for them for the next three years, and then depending upon their priorities and budget, they will then accordingly adjust what items they want us to work on or they want their internal teams to work on. And we're not in it to make the bug, we are in it to make sure that they're successful in transforming their company to the next level using automation and workflow automation.
[00:16:12] Speaker D: So tell me about specific emerging technologies that you're excited about, say, for the next five years or so to incorporate into your solutions.
[00:16:25] Speaker C: I think, as I'm sure everyone knows, generative AI and large language models, they are the next big things. Dental is starting to catch up.
We have access to a lot of data, de identified patient data, and we're looking at can we have AI start to understand the patterns and then create some sort of projections for the dental offices to say, oh, by the way, I analyze all this data and we feel that because we have analyzed this data, there are certain things that you should do that you may not know about it because you're not looking at the whole picture. So it's just like how generative AI works today. You're feeding a large amount of data into it and it's coming up with, you know, almost like an answer bank. And you ask the right question and you get the right answer without doing a lot of investment. So we feel that with the data, the dental data as well as the data for insurance verification claims and for payments, there is a lot more things that AI will be able to do to solve a lot of problems. And so that's, I feel that that's going to become the foundation of, of the future of dentistry.
[00:17:46] Speaker D: Definitely. Good.
[00:17:50] Speaker A: Certainly on board with that. Rajiv, setting AI for clinical applications aside for a minute and just kind of getting back to what you're talking about in regards to generative AI related to revenue cycle management, patient engagement, patient communication of the dsos that you've either come across or collected kind of industry insights from. How many would you say have AI as a business initiative on their priority list for the upcoming twelve months?
[00:18:26] Speaker C: Like you said, if you put aside the imaging AI, most of the dsos don't even know what the use cases are because they're still trying to solve basic problems of automating insurance verification or simplifying posting payments or simplifying claim management. So there is a lot of that that's still not properly served. I mean, like I said last year when we did this survey, we found that out of those 330 or so dsos that we surveyed, only 10% were using any third party software for insurance verification. The rest of them were using humans to do insurance verification, which if you just look at from a cost perspective, doesn't matter if it's here or in other countries or outsourced. That's huge, right?
[00:19:17] Speaker D: You've worked in other sectors in addition to healthcare. Can you think of some things that maybe the dental industry could learn from other industries in, you know, terms of say tech adoption or you know, things of that nature?
[00:19:33] Speaker C: Yeah, I mean if you look at just medical, I mean there is so much innovation happening over there and I feel that, you know, all of that innovation is starting to take place here now. I mean starting with, started with imaging, now we are dealing with a lot of data that wasn't the case before. If you look at just, you know, patient communication, just a lot of AI is happening in that space. And it's not just AI. You look at robotic process automation that's automating a lot of manual tasks. And we feel that, I think in the next three years as we are projecting that around 45% of the dsos will move from on Prem PM's systems to cloud and all these cloud will be expected to have APIs available and so that means they will give greater access to their data. We feel that that's where the future is going to be.
[00:20:28] Speaker D: So thank you for joining us, Rajiv. We appreciate it. We hope we can stay connected, maybe talk, you know, have you back in a few months, let us know how things are going. Thank you.
[00:20:41] Speaker C: Awesome. Thank you. Thanks for having me. Appreciate the time. Okay.
[00:20:44] Speaker A: Thank you, Rajiv.
