How to Build a Data-Driven DSO with Dan Romary

Alec Goldman:
Welcome to How I Grew My Practice, a podcast presented by NexHealth. I'm your host, Alec Goldman. In this episode, we have Dan Romary, Chief Information and Analytics Officer from North American Dental Group, here to talk about how to build a data-driven DSO. Dan, how you doing?
Dan:
Good, great to see you Alec. As always, happy Friday.
Alec Goldman:
Happy Friday. Dan, just for the folks in our audience who may not know who you are, if you could share a little bit about who you are, what you're up to at North American, and how did you end up in this role?
Dan:
Sure, I appreciate that. Yeah, briefly, so I'm Dan Romery. I'm the CIO and analytics officer here at the North American Dental Group. I actually just celebrated my three-year work-anniversary yesterday. So I've been here in dental for three years now. Prior to that, I actually worked for a number of companies and industries, primarily focused on high-tech data integration engineering, trying to basically build bridges and connect dots between different concepts, departments, things like that, to provide actionable outcomes using data analytics and just kind of bringing together all of the teams. So what really brought me into dental was coming from high tech, as we came through the COVID, I found that, you know, the industry was a little bit sort of shy on things that were sort of cutting edge and bleeding edge and kind of pulled back on a lot of the types of projects that I was working on, which I was managing things like AI and platforms and developing those types of projects. So even some of the largest companies in manufacturing distribution logistics, once the COVID hit, they kind of pulled back some of their investments, which opened my mind to something that was a little bit more stable. I think about healthcare, I think about dentistry. which is an industry which is always going to be there as long as people have teeth. And it's also an industry that I think is ripe for innovation and opportunities for things like the types of principles and concepts that I've been working on throughout my career.
Alec Goldman:
Dan, so being data-driven feels, it's a very buzzwordy term, but you have a really interesting background on predictive analytics. Would you mind just sharing with the audience a little bit about that?
Dan:
Oh, for sure. Yeah. So predictive analytics and just kind of data in general, something I'm extremely passionate about. So starting my career around a couple of decades ago, just kind of dating myself, I worked in, I was working in retail and sort of working on that customer loyalty card program, doing customer skin models, sort of propensity models, marketing models, email, you know, mark, target market campaign models. This is before the concept of even BI business intelligence, a lot of platforms that we're using now, but we were using kind of old fashion regression and sort of just basically data integration, pulling together data from a lot of different environments. I progressed from that role to a number of other industries, retail, publishing, and a couple of other industries, using the same principles of sort of pulling together massive amounts of data, developing different types of models, based on behavior, based on what we're trying to enact and effect for our customers. And using that in dentistry is also something that is not quite as common as I thought it was, because patients have needs and they have... various sort of characteristics and things like that and kind of helping to understand that's important.
That said, I'm also, as I mentioned, I'm very passionate about data. My background is actually as an engineer, sort of data engineer, doing data processing, predictive analytics and things like that. Unlike most CIOs who typically come from sort of that infrastructure and network side, I try to bring a little bit more of that strategic alignment based on sort of clinical innovation in our practices. In my spare time, up until recently for about the past 10 years or so, I was also an adjunct professor teaching sort of big data concepts, data lakes, and more importantly, you know, predictive analytics and statistics on basically bringing data together. And the one message that I try to bring to my students, whether they're undergrads or master's students, is really kind of the principle of kind of keep it simple. If it's not relatable, if it's not explainable, if it's not actionable, then it's just entertainment.
And, you know, people get entertained by all of the really awesome tools out there, you know, from R and Python and all of these other things that have great colorful. color coded maps and things like that. But if it's not something that I could explain to an executive, a stakeholder, a peer in a very simple way, then they're always going to reject it. They're always gonna question it. They're always gonna think that it just doesn't make sense or it's too complex to make actionable. We always have to realize that there's a human element to this. And when we're dealing with patients, we're dealing with dentists, doctors, providers, hygienists, we can't sort of paint the world in data because people become lost and obscured and they feel like they lose that sort of human touch and that human element.
And when I think about data, the other thing I was going to say is, you know, in terms of data presentation, you know, dashboard prediction, what we're really talking about here at the end of the day is communication. We're trying to communicate an idea, communicate a concept, pull together different sort of, you know, nuggets and gold nuggets and kind of build a bridge between something that we may have hypothesized that we were trying to prove or disprove. And that really, in a nutshell, comes down to communications in terms of showing people, you know, what the data is telling us and kind of where the opportunities lie. So That's really kind of how I think about it. You know, if I was going to have a mantra, it would be kind of keep it simple and explainable.
And also, you know, maximize effective communication anytime we're talking about data. Also, I think of AI has been in the news, you know, for the past decade or so, much more realistically now that we have like chat GPT-4, all of the different chat bots, which are completely amazing. Also in healthcare and medical, it's also becoming extremely prominent as well. So as we kind of think about all of the advances, it's that much more important to make sure that we're not obscuring this behind sort of a veil of obscurity that we have the ability to say, yes, we understand it, we know what it can do. It is something that was developed by people and should be explainable by people. Whatever type of input we're putting in there should be explainable on the other side. And that's really kind of the approach I've always taken. As I've developed product managed AI solutions and things like that, I've always tried to keep it very succinct and explainable so that people actually use it. Because if it's... Not explainable people will always question.
Alec Goldman:
Yeah, there's obviously the concept of there's data, there's information, and then there's knowledge and wisdom. And being able to climb that hierarchy and sharing that wisdom and knowledge across an organization is, I think, the difference between folks who are just looking at data and actually communicating and making change within organizations.
Dan:
That's a great point too. And I'll just, I'll echo that sentiment by the way, and saying, you know, when we think about that knowledge and wisdom piece, that's where our experts come in. That's why when we, when I build any type of model or any type of analytics type of project, I'm always partnered with a dentist who's an expert in that particular field or a great office manager who really understands patient communication relationships or a hygienist who really kind of understands how we're connecting the dots between the AI algorithms. So I think that, you know, as we think about communications, I think what you said is extremely important about sort of bringing that expert level of sort of wisdom to help drive the strategies. Data for the sake of data is always a non-stop.
Alec Goldman:
So obviously you have a massive background on being predictive analytics data. What was kind of the mission, or what were the problems that you were solving when you were joining North American Dental? How has that kind of changed the organization, you being in the role that you are?
Dan:
That's a really great question. I think that I came to North American Dental Group right about the right time. I think that I'm fortunate to be surrounded by really smart, insightful leaders. And our leaders have vast amounts of experience in dental and a lot of the types of decision-making that they were making was based on their experience, their instincts, and their gut. And I think that what I tried to bring to solve some of the challenges they had was to have an open mind in terms of thinking out of the box with how we use data analytics and reporting to help to sort of bridge the gaps between. their hypothesis, what they think is going on in the field with what's actually happening.
The other thing that happened in the past few years whenever I came on was, you know, we're coming out of COVID. This is a completely unprecedented time in terms of understanding the human nature in the business and understanding how people's behavior has changed. We basically took a solid year where people effectively weren't allowed to go to the dentist or go to a doctor or go anywhere unless they had an emergency. We were shutting down practices because of the COVID just like everybody else was. And what we saw was massive amounts of attrition.
We saw delays in patients coming back to their scheduled appointments. Maybe they're realizing that they didn't need to go for their hygiene checkup every six months or every three months or whatever it was. Maybe they're realizing that they can do without it. And it was these types of challenges as we kind of thought out of the COVID that we said, hey, there's a lot of questions that we just don't understand about the business. But how does our expansion, the De Novo Mergers and Acquisitions Strategy change based on getting into different territories where we see different types of behavior?
So being able to kind of attack some of those really big challenges and just in terms of what we're talking about is human nature and behavior, which has changed and has been completely unpredictable and unprecedented. Helping to try to use data to tell those stories and connect the dots, I think was probably one of the main challenges or one main focus that we had whenever I came on board and also an exciting one at that because even though I look at data all day, I also am a patient and I have a family and people to go see the dentist. and their propensity to being outside the house without a mask and all of the other elements, it changed the way people behaved. So I can certainly relate to that.
Alec Goldman:
Yeah. So given that you are responsible for overseeing hundreds of locations across the country, being data-driven means tracking each point of the patient experience. So certainly, you know, an inevitable question is, you know, when we're talking about a topic as broad as making a data-driven DSO, you know, can you share about what are the key metrics that your that you're tracking, what does it actually mean for a DSO to be data-driven?
Dan:
Yeah, that's a great point. So I think, you know, one of the things that I think about in terms of being data-driven is I always talk about data-driven culture of informed decision-making, which basically says that, you know, if we're going to try to affect change in any area, we have to have kind of data to back it. So we have some really strong data analytics. I would say it's probably beyond what most companies are doing in dental. I've talked to a lot of the data BI integrator companies. And I think we've learned a lot from this both, but we're doing something different because I'm trying to apply the same principles that I've learned from previous roles. So to be more specific, one KPI that we implemented in the past couple years ago is called the concept of the high watermark, which I'll share with you in just a moment. But just to provide the background of what the high watermark is, coming right out of COVID where we had all of these budgets and we had all these expectations for the business based on the number of appointments, the number of patients we're gonna see, the amount of, you know, the amount of... that we're going to basically realize all the different key metrics in the business that most DSOs sort of revolve around.
We basically had a series of budgets that were forecast based on the past three years of learning, based on all of the learnings we had. Once COVID really hit, a lot of those budget numbers really sort of went out the window. And the practices, the office managers, our operations team really struggled to try to understand what was happening and how to even manage the business. kind of threw away, they sort of threw away what their old playbook, which was, let me share this real quick, I'll just share this on my script. They kind of threw away the old playbook because it made no sense anymore. It just no longer applied. Let me just share this real quick. So what I'm showing you right now is the concept of the high water mark.
This basically says that if I was going to look on a calendar at the number of, the amount of revenue I'm expecting, the number of appointments, the amount of other behavior like no-show rates, capacity, confirmation rates and things like that. Comparing to a budget made no more sense and the teams and operations basically just threw away the budget. So I came up with this concept called the high watermark which basically says if you look at the chart at the bottom here, I'm just going to take a metric here. I'll split this. This is the number of completed appointments. So the concept came from, you know, I'll scratch my head one day. Like a lot of people, after the holidays, I scratch my head trying to figure out how to get myself to do the behavior that is desirable. I was trying to get myself back to the gym, back to doing the things I should be doing. I'm saying, if I could only do what I did during that third month of March when I was just killing it, that would be great.
Rather than compare myself to some false idea of something that's not realistic in a book or in a pamphlet, I said, well, if I could just be the best that I can, I will be superseding myself. achieved past that, that would even be better. So this is taking the concept of complete appointments. Again, this is data that's completely obscure, it's coming from our development environment. And this dotted line here at the bottom here basically says this is your high water mark for complete appointments on a 12-month rolling basis. So we eliminate seasonality, we eliminate anything that happened more than a year ago. And we're just saying, we're not asking anybody to compare themselves to a budget which may or may not make sense. We're just asking you to compare yourself to your best possible behavior. And that's basically what this does.
So if you look here, we can see that, you know, we have this sort of color-coded percentile idea of the number of complete appointments in this sort of dummy area. And in this case, we hit that high watermark a few weeks ago, back at the beginning of July, right? And before that, we actually had a high watermark in the middle of April, as we can see here at 6.9. So as we sort of progressed, we redefined a new standard. We basically raised the bar and that became the new high watermark. So the chart at the top here basically says... at what percentage of the high watermark are we? How far are we in terms of deviation from that particular high watermark? And taking that principle and using that for, you know, our reserve and backfill rate to effectively, you know, double book appointments, capacity rate, how much utilization we're seeing, what's our no show rate that we're trying to decrease, and then the number of confirmations.
That helped the practices to make much more actionable things that were much more obscure before. And effectively, they just threw it away and said, but we're just going to go to work. and do what we've always been doing anyway, because we don't really see how we would ever compare to a budget that literally has become meaningless. So I think that's an important one. That's something that we try to be extremely mindful of. And any questions on that, by the way, Alec, before I jump to the next idea?
Alec Goldman:
Yeah, I mean, I know you've mentioned this to me in the past, this concept of the high water mark. And I think it, you know, at its core, it's almost like you should not compare yourself to anyone else. Just compare yourself to who you are right now. And how can you make a very actionable difference to getting to a state that's better than where you currently are? So I think
Dan:
Try it.
Alec Goldman:
it is extremely helpful from, you know, knowing all the variables in your own life, and then saying, well, What are the five things I could do right now to try and reach that state? What was I doing on that specific day that allowed for me to get there? So I guess the question that I would then throw back to you is if you're seeing that your high watermark on completed appointments is 650, and you're at a current day where it's at 450, what are some questions that you and the team are asking yourselves?
Dan:
That's the question, by the way. So when I think of, I try to always empathize with anybody who we're creating a solution for, whether that's an extremely busy, overworked office manager who has to deal with customers on a daily basis, some of them happy, some of them rude, some of them not happy, or an operations manager who's trying to juggle dozens of practices at a time. I try to put myself in their position and say, look, if I was in their shoes and I had a million things to do today, I don't wanna have to worry about one more thing. If anything, I want my life to be easier. I don't have to worry about how to sort of hit some imaginary budget number.
So to answer your question, the great question is, how do we get from 450 to 600? And the idea is, hey, if I look at my high watermark week, you know, and this is my Apple watch with the, you know, number of steps I take every day, I just say, what was it that I was doing three months ago that allowed me to do that? What was it at the practice that allowed us to achieve that? Was it that we had an extra office manager to help support? Do we have to make phone calls? Do we have an extra hygienist to help open up another slot? Do we have an extra provider? And what's different about the situation now? Did we lose something? Did something change in the industry? Did my top hygienist get sick? So those, or did my doctor go on vacation?
Like, so these are the questions. And some of the questions, and some of the answers to that are very trivial. Like, it's just like, oh yeah, well that week we did this promotion. It was a... you know, come in and get a free toothbrush or something. You know, and I was like, oh, well, let's do that again. You know, and, but that's the thing. It's like, what was the environment like to allow for that level of productivity? And how can we sort of try to emulate that and support it? We're not looking to do anything that's not sustainable. We don't want to say like, well, you guys crushed on the park because we had all of these incentives and you know, doubled the extra bonuses and all the push and all that. We're just saying, we just want you to be your best self and you've done it before. So like, let's try to do it again.
Alec Goldman:
I think it's so great. I mean, essentially what it really looks like is that you're creating a mirror, not just for an individual or an individual practice, but for an entire organization of hundreds of practices across the country.
Dan:
Exactly. And, you know, just echoing my earlier sentiment, like, there's nothing complex about this. There's no prediction, there's no regression, there's no forecast, there's no prescriptive analytics that tells you what to do. It's literally, I think that the effectiveness is in its simplicity, right? Because we're just saying, like, we want you to be your best self, and we can show you what that looks like. And that's basically the message. And I think that as I've sort of deep dived into all of the bringing together all of the variables from every single data source. And every, you know, we're at DSS, we're reliant on dozens of data platforms and platforms to run our organization.
Everything from online scheduling, NexHealth, online billing, open accounts, receivable statements. We have dozens and dozens of platforms. Every platform I work with are the engineering teams and I sit down and we find a way to build an API. And then we do the integration and figure out how to connect the dots between practices, providers, patients, and everything like that. And it's that simple. It's really sort of just based on bringing the data together. Ultra simple approach. Again, simply, you know, high watermark just says, everybody who has ever had an Apple Watch and try to, you know, get their 10,000 steps in, figure out what they did last Tuesday. Like, it's a great exercise, and people relate to it well.
Alec Goldman:
So let me play that back just quickly. So it sounds like what you're doing is making sure that you are standardizing the technology decisions across all of the practices for solutions, again, online booking for NexHealth and maybe payments, reviews, reminders, ensuring that they're all using the same thing and then having all of that information plug into a centralized system.
Dan:
Exactly. It's exactly right. Enterprise data warehouse. And then that way we can develop different types of outputs, analytics, reports, ad hoc reports, connected workbooks, things like that. Exactly. But the integration is really kind of what the value is in my opinion. And that's been the same principle that I've always had since, you know, I started my career decades ago when we were doing, you know, customer skin models, customer loyalty, and, you know, the, the principles are very similar. If I, if I sort of. pivot to the kind of the next concept real quick, because that's sort of, I think that kind of opens up the idea of another concept that we're extremely focused on is something that we think of as, let me see, just let me see, let me just see if I can share my screen real quick.
So the concept, so going to the high water mark, the idea is if I was gonna try to attack another important problem, it's no-show rates. So most DSOs have a no-show rate that, you know, sort of ranges from 15% to 20% to 25% across the board. If you think about a no-show rate, these are people who don't show up for their appointments before they cancel the day of within 24 hours. And we're basically saying is, I had an appointment, I got a reminder, I confirmed, I didn't confirm, I didn't show up. So what you have is an open operatory, you have a dentist, you have a hygienist, you have maybe a dental assistant who basically is not idle. Unlike, you know, what we think about as like retail. where you open the door, the more people that come in, the better we're selling something. This is a very specialized service that we're providing.
So we reserve that chair for people. So for every patient that doesn't show up, obviously it costs resources, energy, effort, right? It's very expensive. So just kind of looking at this screen, this is a list of the appointments for the day. Again, dummy data, looking at sort of our sandbox environment, and we created an algorithm. Basically, it's a no-show score. It's a rating. based on the propensity for patients to not show up. And what that means is, we know a lot of information about our patients, and we can start to think through how to use that information to kind of create a predictor of those that are more likely than less to not show up. So of course, being kind of a statistician, data junkie, the first step I took was to take every single piece of information I know about our patients, whether it be the proximity, the distance from the practice that they live. you know, are people in that community more likely to take public transportation?
Are there other factors, you know, how many households potentially have families versus single versus married? Anything that could kind of give me a towel in terms of predicting human behavior, what's the, you know, what's the relative, you know, market demographic and other, I pulled together literally dozens of variables to try to create a predictive analytics score. And what happened was to my, to my good fortune, two factors bubbled up to the top that were accountable for over 90% of the predictive power, strength of my predictor. And they were whether the patient confirmed their appointment and what was their historical no-show rate, that's it. That's all I needed to know to create a score. So in this case, we can see here this dummy patient potentially missed 50% of their 12 past appointments, didn't confirm, so therefore we say that they are A5 which is the top level of person that could be you know That would not show up for their appointment If I was taking those odds to Vegas and I knew that this person had a very high percentage not to show up I would probably be able to make a bet and be right So we introduced this keeping it simple the beauty of it is I'm not showing them AI a black box 27 variables that went into determining this I'm showing them two things that they are tangibly seeing So the first thing that people asked was well I don't know if the 50% is true.
I just saw Mr. Jenkins last week and he typically shows up for his appointments. I said, okay, well, if we drill into that particular patient, here's exactly what they did over the last year or throughout the history of us knowing that person. We, here's the status of every appointment they had. They missed their appointment, they broke it, they canceled within 24 hours. They said, okay, that's fine, but you know, I'm still not comfortable with, you know, potentially double booking or putting somebody into a, you know, a backup slot. you know, like they do on airlines and things like that. Because what if Mr. Jenkins shows up after all? I said, well, he may show up, but we also know that based on our historical trending, this is again, dummy data. This is not looking across enterprise rank. But these numbers have been consistently accurate, just based on our algorithm.
So this is our own algorithm. But anyway, we basically know that looking at those factors, based on that one to five scale ranking, that particular page used a five is probably two thirds likely cannot show up for that appointment. So we had to kind of play it back to the office managers and the teams to sort of inform them that by keeping it simple, by sort of having a way to sort of predict more or less likely who may not show up, we can actually have a powerful tool and sort of navigating those no show rates confirmations and how to effect the type of change that we want. And if we can kind of leverage, in some cases, back filling appointments or double booking that appointment. and we can manage it because we have multiple providers. At least we have some strategy around which patients have that higher propensity to not.
Alec Goldman:
Yeah, I think it's incredible. I mean, what you're effectively doing is treating your seats at your practices, more or less like hotel rooms or airline seats.
Dan:
Right. And at any point during the either of those discussions, did I talk about, um, logistic regression or AI or black box?
Alec Goldman:
No, that's simple. You
Dan:
Right.
Alec Goldman:
can do two variables after. I mean, you obviously did your homework and your analysis, but
Dan:
That's right. Yeah. That's right. And that's a good point. Like we did the analysis first. We did the homework first and it proved that we can, that we have things that are very actionable. Human nature is not extraordinarily complicated. And so we want to try to keep it simple just to say like, you know, if somebody just doesn't show up to their appointments, then it's not a value to them. We can at least tell them to this folks.
Alec Goldman:
Yeah. So obviously having that type of reporting is, I would say it's invaluable. But you mentioned earlier in the show that lots of dental service organizations may not have something like this. I wanted to ask you, what are the challenges that you think DSOs are facing to implement more of a data-driven approach in their organization?
Dan:
That's a good question. I personally think it's just to get to that level of, to get to that level of sort of maturity, it's, you know, there's a maturity model when it comes to sort of being data-driven, right? There's, you know, some of these that have some basic data reporting, relying on kind of the vendors' dashboards and things like that to kind of lead the way, which is fine. That's okay. I think that where you kind of cross that chasm is where you actually start to do that integration. So, you know, if we were reliant on our vendors' dashboards, we could certainly tell a lot of information fact that rollout was.
But by actually adding that data to some of the other information that we have like from our practice management system and things like that, we can actually start to get those insights of saying like, oh well you know based on this we can see that, based on the difference between new patients and existing patients, booking appointments online, we see a different type of behavior, we also see a different type of profile. And that only happens whenever we integrate the data. So I mean the short answer is it requires a the DSO would really be required to invest in kind of that engineering team, you know, to actually sort of do the work and not sort of outsource it or rely on vendors.
I think that, you know, as I, as I've sort of been in healthcare for a few years now, I do see a higher propensity for CIOs and companies to invest in the platforms and partner with a lot of their really good vendors. But I don't always see the, um, the appetite to sort of invest in engineering teams because, you know, it's, it's a relatively traditional business. We're not, typically think of anything like high tech or kind of going outside of how the business has been traditionally run per se.
\And frankly, a lot of vendors provide great solutions with reporting, so there isn't always a need to do that. So I think it's a conscious decision, it's a conscious investment. It's honestly what attracted me to this particular company, North American Dental Group, because they did value a sort of culture of data-driven decision-making. They just didn't have all the tools in place before I got there, kind of orchestrate some of those things. But I think the challenge is just making that conscientious decision to be able to do that. And look, it's an investment and it pays off after it takes time for it to pay off. And some of these principles aren't always well defined in terms of like how we kind of build some of these models and things like that. Unlike other industries where, you know, like in retail, having a customer database is just a given, right? In dental, not so much.
Alec Goldman:
Well, certainly just, you know, even looking at the dummy data with you today, um, I feel like North American Dental Group is obviously set up for success and perhaps other ways DSOs are not just by having someone in your role. Um, and obviously the team that you built.
Dan:
I appreciate you saying that. And I can't take credit at all. I think that the team that I have that I get the opportunity to work with has been amazing, you know, just in terms of very talented, bright sort of developers and engineers. Also, I have to give the credit to the rest of our executive leadership team for having faith and confidence in continuing to make the investments in innovation and clinical innovation. That's what inspires me. That's what kind of brings me back. That's what brings everybody else back. Because if they if they didn't, I think that we would feel a little behind the time.
And we think about even the competitive workforce and market, everybody from, you know, front desk managers to dentists. People want to work at a company that invests in clinical innovation. You know, we have dentists coming out of school who are using all of the latest, you know, lasers and 3D printers and AI radiograph technologies. They don't want to go and work for a company that isn't investing in those types of technologies because it doesn't isn't going to help grow their career. You know, our company is fortunate enough, but we're making those investments.
So in my role, in addition to some of the data strategy, which we talked about a few minutes ago, I'm also responsible for helping to support, roll out and align on rolling on our AI platform for radiograph overlays. That's something that's extremely exciting. 3D printers, panoramic X-ray machines and some sort of all the latest technology. So I'm extremely grateful that we have an executive leadership team who sees progress and investment. And I'm also extremely grateful for the engineering team that helps to sort of lift us up every day too. So I take no credit for anything.
Alec Goldman:
a little bit. Dan, we're at the 29 minute mark. I do want to ask one last question, which is just any last second, last thoughts, last advice that you have for other folks at DSOs in the dental industry to maybe start embracing a little bit more of that data-driven approach that you brought to North American Dental Group.
Dan:
Yeah, I appreciate it Alec. And you know, I think that what I would say is, you know, a lot of people think of innovation and AI and as sort of this all-encompassing thing, right? It's sort of like boils the ocean in a heartbeat. That's not what it is. It's much more precise and you can start small and make huge, huge strides. And everything has an 80 20 role in terms of value. I go back to my original sentiment, which is, you know, keep it simple and keep it explainable because what we're really talking about here is communications, sure it's communications via technology, but it's human communication. If I can't communicate effectively to somebody about either any of these things, a high watermark or a patient score, it wouldn't be used. And the fact that it would be completely ineffective. So I mean, I go back to my original mantra, which is keep it simple, make it explainable. If I can understand it, anybody can understand it.
Alec Goldman:
I think it's a great lesson, not just for dental, but you know, I'd go as far as any industry.
Dan:
For sure.
Alec Goldman:
Dan, thank you so much for doing the show, come and prepared, show the dummy work. Honestly, it was one of my favorite episodes, just walking through all the data with you. So thank you so much for joining today.
Dan:
Thanks, Alec, I appreciate it. It's really my pleasure and appreciate all the help and look forward to talking more soon. Thank you so much.
Alec Goldman:
Thanks, Dan.