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Where do business insight and patient experience come together?
In this week’s episode, Dr. Hariton shares his POV on cost reductions, business opportunities, and what fertility tech gets right (and wrong) when designing for doctors and patients alike.
We talk about:
70% or 10%, differences in IVF conversion rate
How to reduce patient drop out
How to measure real IVF conversion rates
Where Cercle fits in the fertility tech stack
How to balance human touch with scalable systems
Unlock Dormant Data. Stop Patient Churn. Automate Data Work.
See Why 1 Out of Every 4 Clinics Use Cercle
See an increase in patient conversion from 20-40%
Consolidate your vendor stack, save money: stop paying data lake, data warehouse, powerBI, and AI vendors separately
Free up ⅔ of embryologist and nursing time by automating administrative burdens
Stop losing 24% of patients after first failed cycles
Predict higher live birth rates for 26% of patients:
See how US Fertility, Ivy Fertility, and others utilize Cercle’s AI platform to revolutionize their business insights.
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Eduardo Hariton (00:03) once we break through that bottleneck of how many retrievals we're able to do, once we make people have the tools to be able to be more efficient without sacrificing the patient experience or the patient outcome, which are the two important things, then I think we're going to start to see. that cost of IVF starting to drop. And then we have to pass it on to the patients. And I think when we think about passing it on to the patients, I think that pressure is going to come from managed care. Cercle is an artificial intelligence company and their core competency is to use AI and agents to clean data. So they will go into an Excel file with a bunch of patient data or PDFs or JPEG images and they will sweep through that data and they will extract and they will structure.
Griffin Jones (01:10) Where do business insights and patient experience come together? In the mind, body and soul of Dr. Eduardo Hariton I'm pretty sure. You're just blowing sunshine because he's your friend. He's my friend because he continues to impress me. I think you're going to find this conversation valuable, not just because he talks about how to reduce patient dropout, how to measure real IVF conversion rate, why some doctors might have a 10 % IVF conversion rate while others convert at 70 % and the new tool in the US fertility tech stack called Cercle that they use to standardize data across EMRs, but also how to set expectations with patients and how to elevate your support team in the patient's eyes. I do see an aversion in some fertility docs to adopting new technologies. Not you, obviously. You're a savant, the voice of a generation and an innovator and early adopter. Other people, of course, I'm talking about. Not because they're afraid of it, but because they're afraid it will limit your human experience with your patients. And that's a fair concern. Even though I like to repeat everything that should be automated must be automated and everything that should not be automated must not be automated. I think Eduardo shows you his colleagues. how to maximize technology and maximize humanity. Of course, I beat the same horse that I do in every interview with Dr. Hariton I'm one year away from winning a five-year bet that IVF costs wouldn't go down. Eduardo has thoughts on what will happen when they do. So yes, you'll hear his thoughts on business opportunities for CRMs and EMRs in the fertility space. But more importantly, in my opinion, he gives you insights as to how to continue to raise the standard of care and patient experience even when you have to be able to serve more patients with less people.
Griffin Jones (02:53) Lastly, Dr. Hariton would like to disclose that he's an advisor to Cercle who's mentioned on this podcast episode and is usually the case on Inside Reproductive Health. His opinions are his own and not those of his employer. Griffin Jones (03:27) Dr. Hariton Eduardo, it's good to have you back on the Inside Reproductive Health podcast, my friend. It's been a while.
Eduardo Hariton (03:34) It's good to be back.
Griffin Jones (03:35) That's probably your fourth or fifth time. I want to talk to you about data management. You've got a lot of different roles. I want to talk about data because you've started to dig in more into the problems going on. How would you describe the pain points that clinics and clinic networks are facing with regard to business insights and data management?
Eduardo Hariton (03:58) I would probably, if it was one word, would say they're numerable. I think there's pain points across the whole system.
Griffin Jones (04:05) That's also a great way of getting out of when somebody wants to ask, button you down for a one word answer. You just say, numerable, innumerable. Yeah.
Eduardo Hariton (04:13) Yeah, innumerable. That's even a better word. Thank you. This is my second language. I'd say the pain points are across different verticals. think, you know, the one that's more salient to me, it's clinical. It's the acquisition of the data that we need to take great care of the patient. They come into the console typically with no data at all. So you have 45 or 60 minute books with a patient and you know nothing about them other than what they're telling you. Maybe they send you records before, maybe they have something, but often they don't. And you're a clean slate at the moment where you have the most time. And then you have to go and manually acquire clinical data by either testing them or going to lab court. It all sits with the patient who may be very motivated or may not be. It often comes back in PDF format where someone has to manually enter that information and that can introduce a lot of errors. And then you have to be able to visually process all that information as a clinician and then make the right clinical decision. And all this lives within an EMR. Some of them are great. Some of them are not so great, but the data flows are not necessarily automated. They're often manual. They're often left up to the patient. And there's a loss of control at the clinic level of how that happens. It's also very expensive to pay the personnel that it takes to move a patient through the clinical team. And that's something that I think might be better over time. there are data playing points on the business side. So we have probably top of funnel CRMs or at the clinic level where we have leads that come in through our websites through the call center. They talk to someone, they book an appointment and then they get to the physician. But there's not a ton of CRMs at the clinical level. Like where is my patient? Did they do their work up? Have they converted? Have they not? To some degree we get that data. we get it at monthly time points or longer. And I'm speaking generally from what I've seen across different clinics, different networks, but not everybody knows what their patients are. I can't pull up a sheet that says out of the 50 patients you saw in the last X period of time, 30 % have completed diagnostics, 50 % have converted to treatment. It's not a clear thing that I can see all the time. And having those KPIs is important for me, it's important for my team. And really, to nudge those patients along that diagnostic journey where you're trying to get testing from them, it is important to understand where they are. It is important for the clinic to know where are patients dropping off and how can we support them? Is it something where someone drops off because they got pregnant? That is the best case scenario. That's what I always want to hear from my patients. If they dropped off because they couldn't figure out how to upload a PDF, that's not great. And we need to be able to understand that. And there's also the financial side where The financial environment is very complex for the patient. There are cash-pay patients, there are insurance patients, there are insurance patients that only have partial coverage, and they all care about the money. And they all want to be able to know that they can afford this treatment. And we need to be able to tell them with a very complex insurance system how much things are going to cost with my very complex clinical plan. Are we doing hatching? Are we not doing hatching? How about XE? Does it cost more? What about my transfer? So you have patients that want all this data, you have people trying to get that data and a lot of manual steps in between of getting the right number to the right patient at the right time. you know, that's just like a little bit about how data has to flow through this whole system in order to do right by the patient clinically, you know, run an efficient operating clinic and also provide the right financial support to those patients. and it's difficult, it's really difficult to make sure you have the tech infrastructure to support that.
Griffin Jones (08:05) With regard to the clinical, you got all this manual data entry. You're getting info from the labs. You're getting info from previous medical history. Oftentimes it's PDF. Oftentimes it's paper that's scanned and then entered. And you said that this all lives in the EMR. But am I inferring too much by thinking this probably doesn't all live in the EMR. There's probably lots of stuff that doesn't make it to the EMR that should and that records are often incomplete. And in some cases, you might not even know it's incomplete. Is that the case? Or do you find that usually do have all the complete information?
Eduardo Hariton (08:44) Well, I guess it depends what you mean, but makes it into the EMR. So when a patient sends something, it gets uploaded as a file. But if you have 15 PDF files that comprise 150 patients, they're technically in the EMR, but they're not in the format where I'm able to access it. So it often has to be uploaded because we need that data. We need to prove that this patient does not have hepatitis B before we start treatment. But it needs to then go into a checklist where everything's together and that's kind of the visual place where it could be a checklist, could be a laptop, could be whatever you call it in different EMRs. So it's not so much that the difficulty is to upload the PDF, the difficulty is to make sure that when you get that data that it goes into the right space in the EMR so that we know that that patient has met all the requirements. You know, when you look at, I work in California and in California we have a lot of Kaiser patients and when you ask for records from Kaiser, they send you everything from like the infected toenail and H9 to the ED visit for a broken finger to their fertility workup. And it can be 800, a thousand patients, pages of records for that patient. So that patient has to go through a or not that patient, that case manager or nurse or admin has to go through and pull out all those records. Or sometimes I will tell you it is the physician. I will get a thousand patient pages of records on Tuesday night for a patient that I'm seeing Wednesday and I have two options. I can spend an hour preparing for the patient or I can show up and say, hey, sorry, your records were too long. I didn't want to spend that time. And I often do the former because I want to be there for that patient, that patient's making time for me. And if I go to that appointment and they send me the records and I'm not prepared, it's not a good use of their time or mine because eventually I'm going to have to read those records and understand it. There are systems that can process this information for us. They're new, they're expensive, but I think they're gonna be the future of how we ingest data to some degree. Right now, that process is very manual and it doesn't help me to have a long PDF because it's not in a way that I can process and use it to take care of that patient. Someone has to go through it and extract the valuable information.
Griffin Jones (10:55) What information specifically is most valuable to you?
Eduardo Hariton (11:00) On the clinical side, it could be everything from lab work to did they check their tubes? So it's often HSG's, it's prior ultrasounds, it's pregnancy history, it's any sort of diagnostic workups, surgical up reports. So it's everything that helps me understand the history of that patient that's in front of me. And oftentimes it's things that have happened in the past. People come to us with recurrence pregnancy loss or it's their prior pregnancy history. Sometimes
Griffin Jones (11:01) Yeah.
Eduardo Hariton (11:29) they often come with outside treatment. So they come for a second opinion, they've moved cities or they want to start fresh, something hasn't worked. And you have to really understand what happened before in order to help them. To start fresh with no information is to really neglect what can help you make that better. You want to know what didn't work or what the history is to try to make it better. And if you don't get that information, then you're doing a disservice to that patient.
Griffin Jones (11:58) What is it that you like to say the patient that I can help the most is the one that I know the most about? Something like that. One, what is that saying? And two, why do you feel that way? Because it's not clear to me that every doc feels that way. A lot of docs just get them in and then they send them for tests after and we'll do more in the follow up. So tell me about that.
Eduardo Hariton (12:18) I always say that I always spend the most time with the patient that I know the least about. And that's my biggest pet peeve about how we design this journey. You have a blank slate of a patient either because they haven't done any testing or because they did do it, but you didn't get it ahead of time to prepare. So you're sitting there spending all this time with the patient talking about, well, you know, if you have sperm, we'll do this. But if the sperm's really low, we have to do IVF because a UI might not work. And you have these circular conversations when you actually have the time to have a very clear, you know, path laid out for them. So, so that I think is a pain point. I personally think that as physicians, we have to understand the most we can about a patient. It's not only that they fail and they're in front of you and that you want you to take care of them. But if you don't understand how they failed and what didn't work and which step kind of dropped off, you're probably not providing your full expertise on how to make it better. You're just throwing the same pitch. and hoping that it works. So they can often tell you something about it. Some patients are great historians, but often they're not. And often what they tell you when they actually get the records and review them is very different than what they remember. And I don't blame them. This is complex, but it's important to really understand that. So in a perfect world, you either get them worked up ahead of time and get that data to make the right informed treatment decision, even if they're not coming from somewhere else. or get those records and ideally get them processed in a way that makes it easy for me to be able to see what I need to see, help make my recommendation or treatment plan, and then move on without having to review hundreds of pages of records myself.
Griffin Jones (14:03) And when you say that you're not providing the best treatment and that you're not providing the fullest extent of your expertise, you're still mostly referring to clinical outcomes. There's a large patient experience element to that too, that people get frustrated when they feel like this person isn't understanding. I told him this, I told them that, I sent them this and they're still asking me these questions or they're maybe sending me down a route that doesn't... answer this question. So you've got an entire patient experience element that is affected when you know less about the patient.
Eduardo Hariton (14:37) 100%. There is nothing more embarrassing than showing up for an appointment and someone asking you, did you review my records? And you're like, what records? Like they didn't make them to me. Like I, I, I look in the different places that I think they are. Sometimes they're like, well, I sent them to you half an hour ago. And you're like, well, sorry, like, you know, I, I, wasn't going to happen, but, if they sent it to the call center and the call center didn't get them to me or they sent it somewhere else, it is really embarrassing. And I apologize profusely. and say, will review this and we will do this again at no cost to you because this is a waste of your time. I don't know this, right? So I think the patient experience part is really important. The other thing that I always start every console, I'm like, I read your history, I appreciate your filling out our form, I know it's long and I know it takes time, but I want to start just by hearing from you. What are the things that you want to make sure that we're covering? this visit and it could be sometimes it's what you expect. Hey, we've been trying for six months. It hasn't happened. We're just here to get your help having a baby. Great. Sometimes it's something that you don't expect at all. I'm really worried about this esoteric thing that I read online. I want to get your opinion on it and you want to make sure that you let the patient guide that console because if you talk about everything you think it's important for that patient and the patient and you don't talk about the one thing that they came to talk to you about. then that patient's not gonna live satisfied. So you wanna make sure you understand what your patient in front of you needs from you. And then that might not be what's important to their care, that's okay. But you wanna make sure you address that so that they live knowing this person really hurt me and they really understand what I'm here for.
Griffin Jones (16:21) I know we're here to talk about data, but I do want to talk about that little aside for patient experience because I think it's so important for the people listening. What you're describing that you do in the consult in the marketing customer experience world, we call that setting the stage. And the reason why that's so important is because there is information and value in asking open-ended questions and revisiting information and the value of that being that people feel listened to. But if people feel like they're repeating information, they don't feel listened to. So you could betray that thing that you're trying to achieve. And I do the same thing with our account managers. If they ask a client, what are your objectives? And then I'll say, we've already had this conversation with them three different times during the sales process. We've had our internal kickoffs to review it. You know what their business objectives are. And they might say, well, we want to make them feel listened to. They don't feel listened to. if they feel like they're repeating themselves. And so what you need to do is set the stage and say, so I've reviewed this and what I understand is A, B and C, but I still want to ask you some more. I want to see if anything's changed. want to make sure that I hear it from your own words. That way you accomplish the best of both worlds in showing them that you've done your homework while still allowing for that open-ended response to make them feel listened to. If you don't set the stage, you... are often shooting yourself in the foot. I think doctors might do that fairly often.
Eduardo Hariton (17:48) I think the other thing just to add is the average doctor will interrupt the patient in under a minute. You're like, tell me what you're really here for. then some patients will talk for 10 minutes. They're there to unload. And you're sitting there and you're like, OK, I'm ready to ask my next question. But I try really, really hard never to interrupt the patient on the first question. they will notice. There's a lot of data about this. Most doctors interrupt patients in under a minute. And it's a really important thing. I learned this from Robby Seddon at RMA of New York. He never interrupts a patient. And it could be one, it could be five, it could be 10. And you see the body language when that happens. And I think it's one of the best tips I've ever gotten. It's just let the patient tell you what they need to tell you. You will get through your history eventually. It's not going to hurt you if it took five extra minutes longer. They will feel much more heard if you just sit there and let them speak.
Griffin Jones (18:45) But some people can also talk without end. So do you never interrupt patients or you just make sure you never interrupt them in that first question and then maybe late in subsequent questions, then you politely redirect.
Eduardo Hariton (18:58) Yeah, I will guide them through it. I try never to interrupt them in the first one. If it's been 10 minutes, I'd be like, that's super helpful. I just want to go back to that one thing you said and then kind of try to redirect a little bit. So it's not never, right? There's like the 95th percentile where you have to move through this questionnaire. You still have a lot of counseling to do after, but I think that first impression when you ask them a question and you interrupt them in 20 seconds, that's not what they're there for. So I have found that letting the patient speak a lot. and telling you how they feel, it gives you a lot of information. You see their body language, you see, are they talking about emotions? Are they talking about clinical? Did they have an experience that they're telling you about that made them disillusioned? That's something you should focus on this new clinic to make sure that experience doesn't repeat because they have PTSD, they have had trauma, they don't want to be here. This is not the fun way to have a baby. No one wants to be with us to have a baby, right? So really letting them go a little bit. can teach you a lot about them and then you can use that to help guide them in the way that they want to be guided, which I think is a really important experience part. Not every patient needs the same from you. You want to make sure that you're giving the patient what they need and making sure that they're a good fit for you.
Griffin Jones (20:09) I think that's a really simple, actionable rule with a lot of benefit. At a bare minimum, never interrupt the patient on their first answer. And it's also something that you can set the stage for. I know it's not the same thing, but in job interviews, I know that some people can talk and some people are nervous and some people will just kind of talk in circles. And so in the very beginning of the interview, I said, we're really excited to get to know you. want to get to know you some more today. and I definitely want to make sure that I understand what's important to you in different areas. So there might be some times where I politely cut you off and I ask a different question because I really want to make sure that I get to know you today. And then at the end, I'm going to make sure that you have time to ask any further questions or say anything that you felt like wasn't covered. Is that okay? And so another way of setting the stage, but you talked about on the clinical side, that you think there might be ways, new technologies emerging that help clean up this data or aggregate this data, move the patient through the journey. Are those different technologies? There are some solutions that will clean up the data, but then patient journey automation is something completely different. What are those technologies? How do see them working?
Eduardo Hariton (21:26) I mean, I think one of the biggest challenges is that our data is siloed, right? You have CRM data, customer information, financial stuff in one system that you have your EMR, which is your electronic medical record in another system, which you use to manage the patient. But most people have like a billing platform and a, you know, software platform. And then you have your, you know, video link, you know, system, however you do your telehealth consult. And then there's probably a financial system. And sometimes they're integrating the talk to each other and sometimes they're not. When you think about aggregating clinical data, there's often some sort of API or bespoke integration between your front end system so that the non-clinical data gets into the EMR and if they change their address, it's connected and that kind of process. And sometimes there's not and they're living in complete silos. I think there are... new companies coming into the market that are looking and saying, there are a lot of issues in how we handle clinical data. And clinical data is particularly hard because it is much harder to train a doctor like me to enter everything in the same exact way than it is to train a front end customer service person. Like, hey, when we intake people, this is our protocol. We follow these steps. So doctors like to chart differently. They have to write notes for their sales differently. So you have a lot of data coming in. The data is as good as what we put into the system. So garbage in, garbage out. If we don't, if people can't understand what we're writing, then we can't make sense of it. And that happens when we're cross covering for each other. When I'm working the weekend and I'm taking care of my partner's patients, we work very hard to try to do things somewhat similarly so that when we're taking care of each other patients, we know what the patient needs. We know quickly. what needs to happen and we can take care of them.
Griffin Jones (23:17) You're not talking about not being able to understand each other's penmanship. You're talking about not being able to understand each other's shorthand. So you get on the same page of what you call things and how you write notes.
Eduardo Hariton (23:28) that and also where we put it. Like if I'm looking where I put it and it's not there, where is it? Is it in this type of node? Is in this know checkbox field? Everybody has their flow they look for where's the AMH? Is it on the treatment plan? Is it on their lab history? And you don't want to bounce around 10 different ways with each patient when you're rounding on 50 or 70 or 100 patients in a day. You want to see that information quickly. So we all have to try to work in the same way but also Across clinics, people enter information to an EMR differently. And when you see it where I see it, which is at the network level, where you have like 20 or 25 clinics, all doing things differently, all some of them using different electronic medical records, and you're trying to say, what are the best practices? Like, how do we make our best clinic and our worst clinic closer together? How do we bring people that might have an issue in fertilization or an issue in conversion or a pregnancy rate issue. How do we understand it? How do we isolate the variables that might be contributing and try to bring them up? Because that's what really is going to help patients. And that's the real value of being part of a large network. It's not that we're like pointing fingers and being like, you're number one, you're number 20. We're saying like, what is it that number one does really well? And can we learn anything from that to go teach number 15 to 20 to help them? How do we bring those best practices? And no, sometimes it's the patients, right? Like some patients have lower prognosis. Some people take care of patients that have more comorbidities, are older, have a lower chance of success, but sometimes it's not. So it's really, you cannot answer those questions if you don't have data. You know, the first step of process improvement is measurement. And you have to be able to measure and you have to be able to measure. consistently and accurately and getting all of that data is a huge challenge with a fragmented system because you can't compare apples to oranges. So it's really a huge investment of time and effort to aggregate all of this data into a single platform that we can use to start asking these questions. And, you know, I'm sure we'll touch on this later, but that can help process improvement, QIQA, setting KPIs. but it also helps the field because it helps research. Like we're very lucky at USF to have five fellowships and two of very smart, hungry fellows that are trying to ask existential questions about our field. And before it used to take a long time to get enough data to answer those questions. If we can give them data from 20 clinics that are working together and we can have it clean for them to ask these questions, they're going to ask them and answer them a lot quicker. We can delve into that later too.
Griffin Jones (26:19) I want to delve into some of that. You told me at ASRM about a company called Cercle. I didn't know them at the time. I just asked you an open-ended question of who I should be paying attention to. I asked David Stern the same thing. And you both told me about this company called Cercle that I know now. But I didn't know at the time. What did they do?
Eduardo Hariton (26:41) So Cercle is an artificial intelligence company and their core competency is to use AI and agents to clean data. So they will go into an Excel file with a bunch of patient data or PDFs or JPEG images and they will sweep through that data and they will extract and number one, they will structure. So they will take those patient variables and put them into what they call the graph, which is essentially a relational database that connects every patient to another patient and connects every data point to each other. So it's no longer in like columns and rows, but in this kind of relational database that then you can use to ask questions for them. And they will also anonymize the data. So they don't care if it's Patty or Jenny or John, they just care that this is a patient. of these demographics that went through a system. And that allows them to ask very complex questions of the data. They're agnostic to electronic medical records. if you have like, we did multiple medical records in one system, they are able to take that data, absorb it from different ones, and then put it in an apples to apples way where we can look across different DMRs when before that would have been exceedingly challenging.
Griffin Jones (28:07) Does that solve for the underlying API issue or no? Because normally you would have to have something that the EMRs are all connected under a certain underlying API in order for that to work, right? Does that help to get around that or is that a different issue? Eduardo Hariton (28:24) Well, it depends on the EMR. they don't necessarily, you know, they could, I guess, essentially take the data, clean it, and then push it back into the EMR. I think there's probably some compliance issues there that you need to think about and like, you know, data quality and be very comfortable that it's not going to change in a way where you're making a decision. But essentially, you can extract it out of the EMR and see it outside of your EMR in a way that is very useful. not necessarily for, you know, hey, this patient, I send them the data, they sent it back and now I'm looking at the data they got me, but rather aggregated patients. you know, one of the use cases that we have, built an artificial intelligence predictor tool, I wanted to be able to give my patients personalized predictions as to what they did. And because we were able to work with Cercle in creating this graph that has our patient, you know, anonymized data. So they don't know what patients what, but they know that 26 year old with this AMH and that diagnosis had these outcomes. I'm able to ask them, I have a 34 year old with PCOS and AMH of 3.5 and antral follicle count of so-and-so. And this is the partner semen analysis. That person's in front of me and they're asking me what's their pregnancy rate if they do IUI and IVF. So I go to this predictor tool, I enter these variables and I'm able to within seconds present them data from the last eight years across our network of how patients like them did. That helps the patient make an informed decision so that patient can then understand, okay, well, IUI cost this, you know, maybe has this percent chance of success. If I'm not there, I'll have to go to IVF. Which one seems more appealing? How do I, you know, what's the right path for me? It also sets expectations, right? When I'm calling that patient and I'm like, Hey, you know, your IUI cycle was not successful. I'm sorry. I don't have better news. Let's try it again. It's not a huge shock because they understand that the success rate of IVF is higher than IUI or that IUI was not going to work in two or three people, for example. So it sets the stage to set realistic expectations. And we started using this recently, so I don't have a ton of data to show for it yet. But I want to understand if when that patient fails the first cycle, that patient sticks around with us for that second cycle, because they understood that was a possibility rather than have this big disappointment. And then they're like, I can't believe this happened. There must be something wrong. I need to go somewhere else because clearly something didn't work out here. I think the expectation setting.
Griffin Jones (31:07) Will you have those business insights to be able to see our conversion to second cycle after failed first cycle has increased using this? Eduardo Hariton (31:18) Yeah, absolutely. Yeah, that's part of why we want to understand. Like we want to know if these tools are helpful to patients, you know, to some degree. Yes, we want people who need treatment to get the treatment that they need. That's an important business metric for the organization. But for me as a clinician, that's, you know, I'm spending an hour with a patient. I want to understand if that patient doesn't stick with me. Why is that happening? Is it because I'm not setting the right expectations? Is it because my team's not working well in the diagnostic phase? Is it because I'm just not the right fit? Those are all things that are okay, but you see conversion of 70 % for some doctors and 10 % for others. So to the same point of like using data to help improve, we want to understand what do the people that convert 70 % of patients do well and how can that help the people that convert 10 % because we want to work harder, not smarter. You know, we have all talked about the bottleneck of fertility is providers and it's a supply-side problem. So if you have a doctor that has to see 10 patients for every one that goes to treatment, you're not using their time efficiently. So this is just one of the tools that can help patients. And I do think it's going to move the needle in how they convert. I think it's going to distill down probability. That's very complex for a patient to understand into a very easy to understand process. But I think ultimately it's part of building that. expectation for the patient to help them know what's next and know what to expect.
Griffin Jones (32:47) Is the Delta really that wide in conversion between doctors 70 % to 10 %?
Eduardo Hariton (32:54) In some, yeah. mean, I think it's not just the doctor there. It's like, know, new physician, difficult market, cash pay versus a lot of managed care. So there are a lot of variables that are there, but yeah, there are, there's a very wide, you know, even within clinic, there's a wide range of what people convert. So that's the same market. And then across the country, there's even wider variations of what folks convert.
Griffin Jones (33:19) 10 % seems like you would have a hard time staying in business. I've seen variation and have seen some with 70 % and probably some with lower than 30 % and then you start to get concerned. It sounds like there's a really big gap there.
Eduardo Hariton (33:40) Yeah, I mean, I think you have to think about someone's practice. And yes, I would not want to be in a situation where I have to see, again, 10 new patients for one that converts, but you have to measure conversion at a given time point. So if you measure conversion at four months or five months, some people take longer than that. Some people have to save, some of it is financial. So it's not like those patients never come back, but they might just not come back yet. Some people have surgical expertise. So they're seeing patients for a different thing and they like operating on endometriosis. So they're seeing three, four endometriosis patients that might not need surgery for everyone that does. So there's an element of clinical variation, but yes, 10 % is low. And I'd say most people probably hover in the 20 to 40 to 50%. Anything over 50 is fantastic.
Griffin Jones (34:35) With these tools, are you getting real conversion rate data now? Because when people used to hire us, they would ask that and I could just help them do it directionally. The napkin math is that you take your number of retrievals for a year and you divide it by your number of new patients. And that gives you a directional conversion rate. But it only works over a longer period of time, like 12 months, maybe six. It doesn't work over a quarter. It certainly doesn't work over a month because... It obviously takes a longer time for some people to convert, but that gives you some directional math. Are you comfortable that when you see a conversion rate for a doc now that you're looking at the true number?
Eduardo Hariton (35:16) Yeah, and you know, we have our own internal tools for this, but the right way to measure conversion is to say, okay, we are, you know, so to speak, let's say we are in June, because I don't know when this is going to air. So if we're in June, we want to know, are the patients that I'm seeing today in June are going to convert by November? So in November, we have to look back and say, how many of the people that Eduardo saw in June have gone to treatment? What treatment have they gone to? who didn't convert and ideally understand why they didn't convert. But you're looking at a time point, it doesn't matter. We could have a super busy June, but those are the January patients, Like December and January are typically slow months for IVF. It's not that our conversion is bad, it's that we often close the lab and don't do a lot of retrievals. So the real metric is at a given time point and every... network or clinic and pick whatever the time point is depending on how their curve of conversion, what the sweet spot is, but you want to see how many patients that you saw X number of months ago have converted by now. That's your true conversion because you're actually tying those treatments to the patient and we do have the ability to do that and we do have the ability to understand how the tracks over time, you know, everybody, you know, fluctuates a little bit, but you see pretty decent trends in conversion as to what happens to a physician. And I think one of the things, you you know this about me, like I really enjoy teaching and like I enjoy working with fellows and I enjoy working with new associates and I'm a new associate. So I don't know the secrets of this is partly selfish for me. I want to understand what do the people that have conversion rates over 50 % do? How do they talk to their patients? How do they run their clinical teams? How do they follow up? How can someone do 500 cycles and every patient loves them and feel like they're their only patient? And some people struggle with that at 200 cycles. You know, it's really important, helpful information to understand, you know, the variations in practice. I want to learn from the people that are super productive because as you get busier, it gets harder. Early on, I can call my patient all the time. I can give them all of the results. And that's what you should be doing to build yourself, to get comfortable and to build those relationships and that kind of marketing army of pregnant patients that are going to recommend you. But as you get busier, that gets harder and you have to pick and choose what matters. So looking and seeing what matters that all these superstars do clinically and in productivity and how will you translate that into things that you can do yourself to run a very efficient practice and how will you give that to the next generation of physicians to give them the wherewithal and the tools and the savviness to think about these things early on, because this is not natural. Like we all wanna be 110 % for every single patient. We all wanna get back to them the same day at the same time and make them feel heard, because that's what the patient wants, but time just doesn't allow us. So picking and choosing the time points where it really means a lot to connect with that patient, I think can give you most of that, but also make you efficient as you grow your practice.
Griffin Jones (38:27) And it's really hard to scale the replication of those best practices without the data, isn't it? Because one of the barriers that I hear to scaling care is docs say, well, I've got to be the one to do this attention, to provide this attention, to do this particular service. And it's not immediately clear to me what's necessary for the doctor to do every single time. But if you could at least see this doc by volume, this doc by conversion rate, and by these docs by volume conversion rate and NPS score versus these ones, then you could at least start to say, okay, there are things that they're doing that their patients are happy with them. And it's obviously not a question of volume. But you need that data first.
Eduardo Hariton (39:14) Yeah, I mean, you don't even know. don't know what the best practices are if you can't measure them. You don't know who the superstars are if you can't measure these things. So absolutely you need that. There's an element of personality too, but I think a lot of it is understanding what those are and what people do. And I think what my sense is from talking to a lot of these folks is one thing that they do really well is set expectations. from the beginning, like I'm going to be your doctor, I am going to be picking your protocol, you're not going to see me through your IVF cycle, my sonographers do this. I'm going to be looking at all the images and making the important decision, but the reason why I'm sitting here with you for an hour is that I have three people doing ultrasounds for me in the three rooms next door, and I'm going to be looking at all of those images as soon as we're done. I am probably not gonna do your retrieval. I hope we get lucky and it falls on my day, but I want the retrieval to be on the best day for you, not the best day for my schedule. So we all take turns doing them and I would let my sister go through a retrieval with any of my partners, they're skilled, they're well trained, but I just want you to know that because I don't want you to be disappointed. And I'm hoping it works out that it's me, but statistically it probably won't because there's 10 of us. And when you set those expectations along the way, The patient's not constantly disappointed. They're not expecting to see you every day, but you have a lot more time to pick up the phone and give them a call here and there, and they get excited. They're so grateful that they get to hear from you. know, Ruhi's taught me that. She sets the right expectations from day one. You're probably not gonna see me. You're gonna see this. This is all my care team. I'm one of them. I'm not at the top. We're gonna all take great care of you. And then over time, you do that. And some of the other really productive doctors have a strong team. nurse practitioners, APPs, strong nurses, clinical coordinators, you're all working together, they need to feel like those people are part of the team and they need to know that most of the interaction will happen with them upfront or they're going to be disappointed and your NPS score is going to
Griffin Jones (41:17) When you talk about data and improvement, the first step being for data improvement is measurement. To what extent do these tools help mitigate the... insufficient input and to what extent is input always unmitigatable.
Eduardo Hariton (41:33) I mean, would say garbage in garbage out always, right? Like if you are not charting correctly, if the information is not there, you do not want this to be made up. There's a concept where you can have smart fixing of emptiness. I forget the official term, but it's essentially like inputting empty values to our best guess. Statistically, you could do that. You do not want to do that for patient data. You do not want to be like, well, her androphobia was this, so we assume her image would be that. Let's just go with that. You have to account for the missingness of the data, and it's OK for it to be missing. But the input part, it's really important. And I don't think that's something that we can necessarily fix looking backwards. I think that's something that we have to improve moving forward and create standards of how we use this electronic. medical records. The part that we haven't touched on is people think that the electronic medical record is a technology and you're just like, I switch EMRs, this one's nicer, it has an app. But an electronic medical record is a foundational operating model of your clinic. When you build a clinic, you build it around an EMR and a set of flows. And when you transition from one electronic medical record to another, That changes how you communicate with your clinical team. That changes how your day works and how you're rounding on patients and how you're taking care of tasks and reviewing them. So your whole flow, you have to take a step back and understand, I, what worked well about that tool? And what do I wanna replicate? And what didn't work well? And what do I have a new tool here? That change management is incredibly hard because it's... changing people. People don't like change, number one, but it's also it might constrain you in a way that you don't want to be constrained. Things that you might have liked about the older one you can't do here because it's just not how it's displayed or solved. So really thinking through how do you use your technology in order to provide better care, the EMR is the central part of that as a physician. And that's why it's such an important decision. What EMR do you use? And is it going to make you more efficient, less efficient? how painful would that change be? And really know that for those first like one, two, three months of a transition, you're going to be swimming upstream because your clinic is just gonna run more inefficiently. But once you hit that velocity, hopefully you switch to something that makes you more efficient and then you start getting like half an hour, an hour, two hours of your day back where before it was really manual tasking and now you can do it more efficient.
Griffin Jones (44:15) Is the reason, is that the reason why EMRs have not really been able to integrate with CRMs that it's not just a software, it's a foundational operating model for the clinic?
Eduardo Hariton (44:28) I don't think that's necessarily the reason. I think a lot of EMRs should probably think about being CRMs in the future, right? Like there is nothing, you I think being an EMR is a lot more complex than being a CRM to some degree, but that patient should come in and flow right through to some degree. So there's integration that has been built between CRMs and EMRs, but I don't think that that's necessarily the reason. I think the people who are building EMRs have a core. core competency in that. there's really CRM is not just a fertility thing. We're a niche industry, we're small, there's not that many clinics. So the amount of interest that you have in building a fertility on EMR is very different than what you have in building patient CRM in healthcare. So we often have great solutions that are used across dermatology, ophthalmology, a bunch of independent private practices or networks or even hospitals that are very good CRMs. they just are not going to go into the EMR space infertility. It's also hard to go the other way because you're competing against very big, well-capitalized players. But in a perfect world, you either would integrate them both very seamlessly or have a CRM function that can take you all the way upstream.
Griffin Jones (45:44) This was always at the top of the list of challenges that we faced when we were doing marketing for clinics, that there was never a good CRM in the fertility space. You talked about maybe the reason why it hasn't gone the other way of EMRs getting into the CRM space is because there's well-capitalized players. Why do you think that they haven't further developed this CRM capability? And we're seeing all these other patient triage, patient automation systems try to come in and do that in the fertility space. I think the EMRs have said that, we have these capabilities. We work with them. They didn't. You could not use them for any meaningful CRM purpose. despite them saying, you can pull this report and that report. Why not?
Eduardo Hariton (46:31) I think eventually some will, is my guess, over time that's going to become more more important and you know, it's going to become a differentiator for whoever does it and does it well. I think the challenge is that it's not their core competency and building a medical record system is very hard. So when you look at the engineers, the front and back and the people building these EMRs, they are heads down trying to make their EMRs better. If it's a new one, there's a lot to improve. If it's an old one, there's a lot to update. So when you say, okay, I have this set of customers, nobody loves their EMRs, so you already have a set of grumpy customers. It's good enough, some are good, some are not so good, some are very bad, but like NPS scores for EMRs, not great, because it's always something about it that could be better. And you say, well, I can use my resources to go build this other thing that then I have to go cross-sell, or I can work on improving my product that... people don't like, but I have to keep them liking, you're going to go to that first one. That's your core competency. That's what feeds you. And you're not going to go develop something else. It takes a lot of investment to do that. And it's a bet. Do I think someone's going to take that bet when they take a step back and look at the whole ecosystem? I do. I think that eventually someone will do that. But I see if I was running an EMR company and I had a list of like hundreds of things that my doctors wanted to make better. and those are the people that are paying for my mortgage at the moment, I would focus all of my attention in building a better and better EMR. Plus you also want to focus your attention in going and building your company by getting more people to switch over to your EMR. So I think people are just heads down in trying to run their companies that they're not stepping back and looking at the whole picture. But eventually I think someone will.
Griffin Jones (48:23) You mentioned the next step after you've improved measurement, then you can really invest in process improvement in QI and QA. How? What are some specific use cases of being able to do that?
Eduardo Hariton (48:36) I think the most clear use case and the easiest one is the lab. We can look into the lab and you have an input which is eggs that a physician retrieves and then you have an output which is embryos that make pregnancy and you have very discrete steps across that process. you know, how many eggs that you exe fertilize correctly. So your fertilization rate, same for conventional. How many blastocysts that you grow. make it to day five or day six, quality of the blastosis, how many of those implant and give pregnancies. So you have clear points, you your thought rate, survival rates, et cetera. You can look across your clinic, can look across physician, we can look across the network and try to understand what are my best performing labs, what are my worst performing labs? What do we know about each of them? Is it the patients? Is there a different inpatient? When you control for the patient coming in and you make it, apples to apples, this age range, know, all using PGT or not using PGT, do we still see those differences and what can we learn about it? And some of them you can learn from the EMR and you can see, hey, and some of them you have to go in person. So we do a lot of like think tanks and flying people from one lab to the other. And we take one lab director, have them come here and go over there and really try to understand those practices to see where we can do and we can move the needle. but you need that granular data as to what's happening. I also think it's important to do it at the clinic level. This was something that predated me at RSA Bay where our doctors have a great culture of transparency and we have a lab director that is very maniacal about measurement. So we would get reports that say pregnancy rate by physician for this quarter and pregnancy rate by embryologist and the combination of both. And we could see I do better, this does better, this person had a bad quarter, but, then, you you have, you know, humble founder of the practice would say, have a lower pregnancy rate this month. Can someone come watch my transfers, make sure that I'm not doing, you know, and, and to me that is exactly the kind of culture that you need to get better. And nothing was different. Everything was perfect. It was a fluke. His patients were lower prognosis and and it happens to me and it happens to everybody else. But you see that it happens to your senior partner, it happens to you. Everybody has fluctuations. What you don't want to happen is you go two years with someone that has 20, 30 % lower pregnancy rate because they're doing something different and no one noticed because no one looked, right? We want to know. I want to be able to tell my patients in that first visit, I tell them, it doesn't matter who retrieves you, you're going to get the same. So I know that when we didn't get a lot of eggs for my patient, I can call them the next day and they're like, I wish you would have been there to do that. And I can say, we measure this. We know that each of our doctors gets the same number of eggs, gets the same maturity. We all see how we do things and we measure that. So if I was there, I would have also gotten four eggs for you. I can say that not just because I'm making you feel better, but because we measure this and we want to know that. And we want to know that the new doctors that join our practice are practicing to a standard of care. We want to know that everybody's progressing well and we want to understand not to be punitive in any way, but to really help them improve. So that culture of measurement, I think predated our practice and my time there. And it's something that I have found super important as we think about quality and quality improvement for a bigger organization.
Griffin Jones (52:17) once you have these benchmarks, how are they disseminated? Am I looking in as a physician, am I looking in a portal and seeing my conversion rate against the average conversion rate or my number of patients or my retrievals against the average number or my success rate or my NPS score? I like seeing all that in some sort of portal against my own data or does US Fertility have a quarterly meeting where you get everybody on Zoom and tell them like, Here's what the benchmarks are. How do you disseminate benchmarks?
Eduardo Hariton (52:49) So I think the each practice gets a report every month that shows you, know, how you're doing, what's your conversion, how many retrievals, how many transfers, all of this, which is just to help you track how you are and how you're doing. And you can see you and you can see your partners. This is not secretive in any way. We want people to understand how they're doing and see how other people are doing. And it's not necessarily to be like, you have to do more, you have to do more. It's to show you like, you know, this is what's possible and this is the range of what things are and you can use that as you want. You know, we want our practice directors to help, especially younger physicians who are growing their practice, understand what those are and help them see, well, if your conversion is an issue, let's think about how we improve. If your new patient visits are the problem, let's get you out there. Let's get you some lunches. Do you want to do a couple of webinars? Like what can we do to support you grow your practice at your own pace? But you need that data. to be able to do that. Every practice itself manages the clinical quality side a little bit different. We have Kate Devine at the top organization looking at it across all our practices, but at each practice level, there's medical directors and there's leadership that can help understand what that means. And you're going to have some variation that is inherent and normal, but you really want to understand, especially for the people that proactively are like, I'm seeing that I'm not where I want to be. someone help me. We want to have those systems, coaching, that mentorship, that data to help diagnose the problem so that we can help them grow. And that can be, they're not getting patients in the door. They need to get out there a little bit more, build those referrals. That could be those patients that are not sticking with them. It's harder when you're younger. Patients want experience and they know when you graduated fellowship and they can see how young you are. So you have to help them feel like they're gonna get top notch care, which they will, but you gotta make them believe it. And that takes a little while to develop and get to know. So we try to give them that data, give them the support that they need and give them that information to empower them to grow the practice that they want to grow and build.
Griffin Jones (55:02) Do you know off the top of your head how many EMRs US Fertility had at one point? You have the US Fertility EMR, but with all the acquisition, with ovation, you've got all these different practices and labs. Are we talking like six, seven, eight different EMRs at one point? More than that?
Eduardo Hariton (55:19) I'd say I can think of four of the top of my head and I think that there are more in some of the clinics that are there. So I'd say if I had to guess, I would say like five to seven, or take.
Griffin Jones (55:35) Standardizing data across that many seems really difficult. Why did you choose Cercle to be able to do that? What did they have or show that made them make sense to be the people to use that for?
Eduardo Hariton (55:50) So what was unique about them was that their core competency was harmonizing the data and structuring it and ingesting it. So to be very clear, this is still a process that requires a lot of input. So when you bring a new EMR on board, we do have to map every column and every file. Everybody calls them differently. They're not the same. They have different scales. have to spend the time making sense of all of it, then we have to QI it, then we have to make sure it matches what they were measuring. And sometimes, you know, that's inaccurate. So it said that they were like measuring it wrong, or it said that we ingested it wrong. Like, what does that look like? So there's a lot of work each time you do that. It's not like you press a button and all of a sudden the dashboard's up. But the cleaning part of it, they're looking through different data fields, they're ingesting, they're making it match. side of things is automated in a way that even in SQL or some of those programs would have been really challenging and difficult to do. So I want all of that process to be sound, but also as automated as it can be. And that's a core competency that we do not have. We do not have software engineer. We do not have agents that we can send into a database.
Griffin Jones (57:05) And is that normally what you would have to do? Hire data architects to build that in SQL or some other language and build that all out? Eduardo Hariton (57:14) I think what you would have to do if you were trying to aggregate it all in the old school ways, you would have this like massive Excel file with the 60 columns you care about. And you would have to have a ton of like pivot tables and macros to bring data from different ones that fit into the right one. And you would have to do that each time. So each time you add it, it would have to be processed. When you do it with Cercle, once they build the, you know, call them the highways. Once the highways are built and you know the off ramp and what data goes where, when it comes this way, it's always has to go that way. Then you can really just give them the same structure of data and get the output already. So a lot of the work is upfront in making sure it's validated and it flows well. But once it flows well, it's gonna continue to flow well, because it's gonna, they already know what highway exit to take for. this piece or that piece.
Griffin Jones (58:12) four years ago, you and I had a bet. Would the cost of IVF increase or decrease within five years? I'm not going to gloat too much, Eduardo. I'm not going to go too much over you. I am going to gloat a little bit more over the people that when you and I were speaking at a conference two years ago. So at this point, we were two years into the five-year bet, not four, but also not zero. So they only had to look ahead to a three year horizon. I asked them if they agreed with you or if they agreed with me. It was 80, maybe 90 % of people agreed with you. And I knew that I was right in that moment after they all raised their hand and said that. But then the comments that they said after, they clearly couldn't figure out how to bring down the cost of IVF. So my question is, why hasn't the cost of IVF come down? And can tools like this or this approach to data... Will that bring the cost of IVF down? Why hasn't it come down? What will be necessary using this technology or otherwise to make it so?
Eduardo Hariton (59:21) I would say I still got a year, but it's not looking good for me. ⁓ It's not looking good for me. I would say because we have a supply side problem. It's basic price elasticity macroeconomics. I think as you have more and more people coming to the system, this is a fixed cost business. The marginal cost of the next retrieval is always going to be the lowest. So we're going to scale.
Griffin Jones (59:26) No it's not.
Eduardo Hariton (59:46) to where we can support that lower cost of IVF. And there are some lower cost models that can deliver IVF, but it's not going to be the way that we're used to delivering IVF. And once we break through that bottleneck of how many retrievals we're able to do, once we make people have the tools to be able to be more efficient without sacrificing the patient experience or the patient outcome, which are the two important things, then I think we're going to start to see. that cost of IVF starting to drop. And then we have to pass it on to the patients. And I think when we think about passing it on to the patients, I think that pressure is going to come from managed care. So if California covers nine more million people in the next few years, we're going to have to do a lot more IVF. But those insurances that are covering that much, they're not going to pay our sticker price. They're going to pay less. And then we're going to have to be able to know, how do I take care of those patients? And with this amount, And David Stern talks about that all the time when he talks about the Massachusetts experience. They're able to do IVF cycles with great outcomes at a lower cost because they're built for scale. They're built for volume and their average physician does more retrievals than the average physician in the United States. So once we remove that supply side and we have that volume coming in, I do think that cost is going down, but right now we don't have that happen. And it's happened slower. than I expected it to happen when I was a Green fellow in fellowship talking to you. But I do think it's eventually going to happen. So I'm going to double down at the end of next year and we'll make another bet. I do think this data will help us lower the cost of care over time. I think it's going to allow us to be more efficient. It's going to not only on the data and CRM side, but on how we manage patient care. It's going to help us meet the patients where they are, communicate that in a way where it doesn't require so many people hours. And the biggest cost in our clinics is not media or icky pipettes, it's people. We have to take care of more patients with less people. And we have to take the tasks that are annoying for those people, like looking through a thousand pages of Kaiser records and automate that, because my nurse also wants to talk to the patient and she also wants to call them with their pregnancy test. She doesn't want to look through their nail infections from 10 years ago. So if we can use the technology to automate the tasks that do not give our employees joy and let everybody work at the top of their license and let someone like me help my patients and connect with them without having to do the manual work, we're going to run much more efficient clinics. We're going to be able to deliver the same outcomes in IVF for a lower cost. And eventually I'm going to win that bet.
Griffin Jones (1:02:36) Well, yeah, because then it's a different bet. So I'm not going to let you double down because I'm not going to take the opposite side of that bet again. I'm not going to take it next year because I am starting to see enough of things in the pipeline to provide that scale that you're describing. But I also don't know that I would take your side of the bet just yet. I'll wait. I'll let the rest of the year finish out and then and then decide if I want to join you on your side of the fence. for next year. But do you think the number one thing is that managed care did not increase to the volume that you're expecting? Is that the main catalyst in your view?
Eduardo Hariton (1:03:15) I don't think that necessarily is that, but I think that we have a system where we have more people wanting our care than we have the ability to deliver that care to some degree, right? There's no waitlist everywhere. There's people that are not busy, but generally you have, when you have a supply side problem, you are able to price how you want and you are able to price at a price that helps you run the clinic. So if someone's willing to pay me X, why am I going to charge them 80 %? I have a lot of cost too. is, you you could call it greedy, but you could call it, well, if I charge them what they're willing to pay me, I can hire that patient experience navigator and I can invest in this technology and I can build the app that they've been asking for a while. So you essentially use that and you invest in your company and you grow. So, you know, I think when, when no one's telling you to lower your price, you're not going to lower your price when people are helped, you know, when When you have pressure, you're going to have to figure out how to survive in a different environment. You know, there's concierge medicine, there's like bare bones medicine, and then there's somewhere in between. And right now we're in a world where we have enough demand that the price side of the equation is not necessarily the biggest variable at the moment. You're able to grow without that. But I think that over time, managed care is going to drive some price pressure. And I know that from looking at what happens in Illinois and what happens in Massachusetts and talking to colleagues there, when the revenue per retrieval goes down, you need to figure out how to operate more lean and efficiently. And the question is, can we bring those models to different markets in the absence of managed care? We can and we should, and we should want to be more efficient to help more patients. But our costs are going up too. So when you look at the clinics, like there's, I'm not going to go into the whole inflation thing, but like our cost to serve goes up. When my nurse goes to the supermarket and the cost are 30 % higher, I need to pay her more and I need to pay my suppliers more and everything has gone out. So the reason why the prices have gone up and I'm not going to inflation adjusted for you on our bed, but when our cost to serve go up. Yeah. Yeah.
The Griffin Jones (1:05:27) We made the bet in 2021, The inflation was already underway. Eduardo Hariton (1:05:34) when you have our costs going up, we have to raise our prices. That's the only way we can maintain our margins and survive. But if the margin is getting squeezed from the top, then we have to figure out how to shrink our cost to serve. And I think the biggest opportunity that we have, and it ties to data to some degree, is technology. I think we can serve our patients with technology a lot better. And I'll pull from David and Abigail, like no one comes to our clinic. One, they don't want to be there. No one likes to have a baby in our clinic. And when they come, they want a baby. They don't want a cycle. They don't want your empathy. They do. like what they really want is to walk out of your clinic as quickly as possible for at least as possible with their baby. And we need to figure out what is it that really matters to that patient and how do we give them that. And that's the baby, but that's also the experience. We can give them just as good of an experience using technology for a lower cost, less people on our side, less operations, less phone calls, more texting, more automation, and still have them work out of our clinic thrilled, grateful, and recommending us without needing to invest so heavily in the people side. And what I want from my people, I want them to do things that make them happy. I walk into my nurse's room. And I see when they had a good day and when they had a bad day and when they had a good day, they're the days that are spending time with patients. They're seeing them, they're cheering them on. They cannot wait to come to those ultrasounds with me because they're living that experience with the patient too. They don't want to be in front of a screen reading records and inputting data and that kind of stuff. So I think we're going to use technology so much better over the next five to 10 years in making that experience better for our staff. for our patients and then using the efficiencies that we gain to be able to open our aperture and say, we are now willing and able to take care of a lot more people and go downstream from the small subset that we're able to serve right now to serve a much more vast population of patients. And that's the right thing to do for the business. It's the right thing to do ethically. And it's the right thing to do for the socioeconomic trends that we have where we're not replacing. our population in terms of the number of families that want and need to have kids. So I think to meet that need, which is an imperative in terms of the replacement rates across the developed world, we need to take that approach and open it up so more people can come in.
Griffin Jones (1:08:06) Every time we talk, I could talk to you for a couple more hours and I think the audience could listen for a couple more hours every time. So audience, if I'm not giving you enough Eduardo, check out his channel, check out Fertility Explained and get some more of Dr. Heriton's insights. Of course, we'll have you back on. Man, I love having you on the program. Thanks for coming back on the show.
Eduardo Hariton (1:08:26) Thanks so much for having me. And I'm glad I booked that extra half hour because you thought we would be done. And here we are, up to the top of the hour. All right. Have a great day. Great to see you. Griffin Jones (1:08:32) Parkinson's law. Thanks, mate.
Eduardo Hariton (1:08:39) Cercle is an artificial intelligence company and their core competency is to use AI and agents to clean data. So they will go into an Excel file with a bunch of patient data or PDFs or JPEG images and they will sweep through that data and they will extract and they will structure.
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