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223 The $1 Billion Project to Automate the IVF Lab. Updates on the collective progress in the R&D Pipeline with Dr. Jacques Cohen

DISCLAIMER: Today’s Advertiser helped make the production and delivery of this episode possible, for free, to you! But the themes expressed by the guests do not necessarily reflect the views of Inside Reproductive Health, nor of the Advertiser. The Advertiser does not have editorial control over the content of this episode, and the guest’s appearance is not an endorsement of the Advertiser.


When will embryologists be robots?

Dr. Jacques Cohen, Chief Scientific Officer of Conceivable Life Sciences, walks us through the research and development currently underway for the automation of the IVF lab.

Tune in to hear Dr. Cohen discuss:

  • The next potential game changing innovations in IVF

  • His opinion on time-lapse incubation and its future in the lab

  • What the FDA doesn’t like about AI solutions

  • The $1B project to automating the IVF lab

Dr. Jacques Cohen
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Transcript

[00:00:00] Dr. Jacques Cohen: You don't go to a dentist hoping that your root canal is going to work or not. You go to a dentist and expect it to be a hundred percent successful. Maybe you got a little infection, but that can be treated, but you want it to be a hundred percent successful. And that's what we want in IVF. We want things to be a hundred percent successful, not 98%, not 80%, or what it is now in some clinics over 60%.

No, we want it to be a hundred percent. And we really want that as soon as possible. So, I think all this technology that we discussed today will play a role in that process. 

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Announcer: Today's advertiser helped make the production and delivery of this episode possible for free to you, but the themes expressed by the guests do not necessarily reflect the views of Inside Reproductive Health nor of the advertiser.

The advertiser does not have editorial control over the content of this episode, and the guest's appearance is not an endorsement of the advertiser.

[00:01:49] Griffin Jones: When will all the embryologists be robots? Soon enough, probably, but that's my speculation. For a more measured walkthrough of what's in the research and development pipeline, For the IVF lab, I bring in veteran lab director, veteran scientific director, Jacques Cohen, Dr. Jacques Cohen, as I should say. And many of you know him very well.

He is now the chief scientific officer of conceivable life sciences. They're working on fully automating the IVF lab. I have Jacques walk us through what they're doing at conceivable at other companies that he's involved with. And I have him walk us through what is preliminary, what's well established, and what's in between.

Is time lapse going to be a must have for embryologists within the next couple years? Dr. Cohen has an opinion. What does the FDA not like about AI solutions? Jacques tells me why and I never knew that. If PGTA and vitrification were among the biggest game changers in the IVF lab in the last decade, what are the next two?

Dr. Cohen walks us through what he thinks might easily be a collective 1 billion project in automating the IVF lab. Enjoy this conversation with Dr. Jacques Cohen. Dr. Cohen, Jacques, welcome to the Inside Reproductive Health podcast. 

[00:03:00] Dr. Jacques Cohen: It's a pleasure being here with you, Griff, and really looking forward to it.

[00:03:04] Griffin Jones: Maybe the pleasure should be mine because people say Jacques Cohen is a legend. Jacques Cohen is a legend. And I've worked in the field for nine years. And I think 2023 was the first year that we met in person. So I'm, I'm interesting to, to see if the legend lives up to the hype in this conversation. But many of our listeners are familiar with you already.

And I wanted to go through oftentimes, you know, sometimes I go into the past cause I'm curious about what led to the developments that got us here. I'm more interested in looking at what are today's nice to haves in terms of what's in the R and D pipeline in the IVF. lab that you think are going to be tomorrow's must haves and tomorrow might mean three years from now.

It might mean 11 years from now, but I want to explore that with you. And so maybe just give us a, maybe we just do a little bit in the past are what are a couple things that were nice to haves in the IVF lab a decade ago that are now must haves that any, that the vast majority of them. Embryologists wouldn't even, you know, want to operate in the IVF lab if they didn't have these things.

What are a couple things that were, were nice to have just a few years ago that are now must haves? 

[00:04:27] Dr. Jacques Cohen: Yeah, well, it all depends. Well, first of all, that depends, and it's a very good question, but it depends on, on where you are in the world. The philosophy, let's say in Japan, where there's a lot of IVF and a lot of programs, and they're very advanced.

It, it, it very much depends where you are. So, in Japan, they would focus on, on strictly single MBO transfer, nothing else is allowed. They would focus on minimal stimulation, which is done in this country, but only in a few laboratories and a few clinics. So it very much depends where you are. In the U. S., I think in the last 10 years.

The technologies have been kind of the same of the years before. It's always hard to give, to have a hard cut, right? Say it's 10 years, it's a 12 years, 15 years, but in, in that ballpark, I see, I think the most important things to do nowadays are, are, are fitifying at the blaster stage, incredibly successful that took, you know, honestly, that took from the early 80s, the first paper on, on, on, on, uh, Embryo fritification in an animal, in a mammal, that, that, that was, that was published in 1985.

And the results, frankly, were intriguing because nobody had thought it could freeze that fast, but the results weren't great. And that's why for many years, decades, really, nobody looked at fritification. And it's only in the last 10, 15 years that that's been implemented worldwide. Uh, and, and nowadays, uh, It's considered a must to have, not only for spare embryo freezing, but maybe freezing all the embryos.

Because one thing that is obvious and has become obvious slowly over time is that the cycles where the stimulation occurs are good for the ovaries and you get multiple eggs. Well, it's not good for the uterus and, or it's not optimal for the uterus. I should say, because of course there are a lot of fresh embryos that have never been frozen and are being transferred for like the stimulation cycles that just implant.

So that's one area. The other area that is very much now driven in, in, in. in IVF in the United States is of course PGTA, pre imaging genetic testing for aneuploidy. That has had seen a slow process as well. I think we're now close to 50 percent of all cycles where PGTA is being performed. So, some clinics, it's completely routine, and they do a big case as PGTA.

Other clinics are more careful or more selective, I should say, and do it maybe in a proportion of patients, whereas in some clinics, it may not be done at all, but the average is close to 50 percent in this country. It's very different from the rest of the world. There we kind of stand out, and this has not been happening overnight.

The data is very good. The data that we have gotten over the years is coming very slowly. There has been tremendous debate back and forth. Debate isn't finished yet, particularly internationally, on PGTA. But we see major advantages of this in this country, and particularly because it gives you a higher chance early on in your adventure as a patient having having MBLs transferred because what is striking with the, looking at the data now from, from SART, what is striking the, the, the, is that A lot of patients don't come back after one or two attempts, irrespective of their economic or insurance situation, they just don't come back.

And so you want to, you want to strike it when the iron is hot. You want to get an embryo transfer now, or the embryo is frozen and you get an embryo transfer in a couple of months or next month or three months from now. That is when people are not just motivated, but not exhausted yet, and yet unfortunately A very exhaustive process and most, and most, and most patients experience it like this.

Not everyone does, but most patients do. I think those are two areas where these are now considered must haves. You don't have to do PGTA in each patient. Also it's expensive and it's, it's, it's cumbersome. It's very time consuming for ambiologists and doctors and nurses. And so we want to maybe do it a bit more selective than some clinics do, but I do think it's of the total package.

It's not going to disappear anytime soon. So those two, fitification, PGTA, and, and they go hand in hand. I think PGTA wouldn't have happened on this scale without the success of fitification. They're very much tied in together. So those are two examples. 

[00:08:58] Griffin Jones: Your point that what is a nice to have in some areas might be a must to have, must have in other geographic areas and vice versa.

Makes me think of what I've been starting to learn about time-lapse incubation. I'm not a a scientist, I'm not an embryologist, so I don't know enough about the cost benefit. But all I can observe is that for some people, time lapse incubation appears to be an absolute must have for some people. It, it, they would, they would never work in a lab that didn't have TLI.

And there are many countries where TLI is the norm, but in the United States it seems like it hasn't really taken off. So can you tell me why that is? 

[00:09:42] Dr. Jacques Cohen: Yeah. Thank you for bringing up TL. I, I probably, uh, I'm, I'm more leaning towards the people who, who couldn't do without it. Not necessarily because I think it improves pregnancy, although I don't see a reason why it shouldn't.

It's nice to leave the MBOs alone for the entire period. Um, you're, you're sitting in, you know, the MBOs are basically in a, it is a robot, it's an incubation robot, and, and they're being photographed every few minutes or. Or every minute and each one at a time, you get a timeless video at the end. What is really, really good about this.

You have a permanent record of that patients and BOS at all times. It also, these incubators have been sought through with so much detail that they kind of are. on the high end side, and they have very, very good results. So, so why it hasn't happened in this country as much as, let's say, some countries in Europe, particularly in Scandinavia, and then England?

I think, I think that is because maybe of the expenditures, and also we are very much data driven in this country, and that's because we have the luxury of looking up our own data. The data of our competitors and clinics in SAR and the CDC, and that is something, don't take it for granted because there is only maybe five, six countries in the world where we have data reporting that is, that's mandated.

And, and, and in most countries, and particularly in Europe, you, you see some data reporting, but it's very, very cursory. And so when we look at those other countries that have data reporting and we compare it, we try to compare it, it's a difficult process because there's so many other factors involved when you analyze data.

But if you try to compare it. I think we're a little better per embryo. I think we're a little better than, than let's say most of the European countries. And I know I'm sticking my head out here and I hope, hope nobody from Europe is watching. But if you are, I think that is the reason why we haven't jumped onto time lapse because all the time lapse, the initial five, six, seven years all came from European countries.

And but I, I think time lapse is, is here to stay. I think this is now the norm. But the reason I didn't mention it is because you set that 10 year limit and time lapse is now 15 years. We've had time lapse for 15 years, hundreds and hundreds of papers. I think it's pretty convincing. Um, things have been discovered we didn't know about before and there's still a long way to go.

So I think time lapse is not going to disappear. Yeah, I think it's the standard to leave the MBOs alone while they're being watched by a machine. It's just a wonderful thing. You don't have to take them back and forth to an incubator. It's, it's, it's, it's an absolute must, but you know, they're expensive and they have to be maintained.

So there's an extra cost as well. I don't know if clinics charge an extra fee for it. I would be, that would be unusual, but maybe, maybe that is the case. I'm not, I don't know enough about that, but yeah, at least it drives up the cost for the clinic as well. Definitely. It's not just the investment. 

[00:12:46] Griffin Jones: Is, is the use of PGTA somehow related to adoption of, of time lapse incubators to that other countries don't, or they use time lapse incubators more because they don't use PGTA.

I've heard something like that, but I don't understand, but I don't understand the rationale. Can you explain that? 

[00:13:07] Dr. Jacques Cohen: Well, there are a few papers that have suggested that if you look at MBO development using time lapse, not using, using the, the archaic manual systems, if you use time lapse, there is a correlation with euploidy.

is normal chromosome detection and abnormal chromosome detection. It's being debated. There's very few papers about this, but that's one of, you hear people, indeed, you're quite right. You hear people say, well, specialists say, you hear say, well, I do, I do time lapse. I don't need to, I don't need PGTA. I hear that less the other way around, but I hear, hear that, hear you say, hear that.

that occasionally, but I think, I think our reaction in this country of not using time lapse is mostly associated because we have the data to show we have so much detail. There's so much information going inside a CDC that's not published in the national report that you do not see in the individual clinic reporting of SARC, which is fairly extensive, very detailed.

It's not, we don't see that in any country, including, including the UK. But it has been data reporting for less, less time, but data reporting nationally has been happening in 1988. It's quite an, in 1987. I mean, it's, it's, it's unbelievable, 35 years of it. And, and if you compare it to our Southern neighbor, Mexico, where there are a lot of good clinics.

There is no national data reporting. That is the norm for, for 80 or 90 percent of all countries in the world, including the ones that do a lot of IVF, including China, where there probably now is much more IVF than anywhere in the world, and including India. But there's also an enormous ton of patients.

Tons of patients that are being treated there, although the accessibility for the country's population is very, very limited because it's all, it's all out of pocket. So it's still a small population, but because it's so many people, there's a lot of IVF cycles being done. None of that is nationwide reported.

We do not know how well these clinics do. 

[00:15:10] Griffin Jones: I want to make sure I understand this relationship between the comprehensiveness of data reporting and time lapse incubation. Is it that other countries where there isn't this national level of reporting where they can see other clinic success rates and the other data points?

Is it, is it they're getting something from time lapse incubators that, They're getting a level of data from time lapse incubators that that they need because they're not getting from a wider pool of data. Or is the United States, because we have a wider pool of data, we're not convinced by the value of time lapse incubators.

I'm, I'm, I want to make sure that I understand the relationship and I don't think that I do. 

[00:15:56] Dr. Jacques Cohen: No, no, and I think that maybe I've, I've slightly misled it, misled you, because, because listen, you need to know the data in order to go forward and understand how well, how, how, where you, where you lag or how well you are doing.

You need to have data, data feedback so that you can compare with your colleagues and other clinics. Time, that's data and actual fact. It's not really entered in the SART data reports in the, you know, that, that would overload the system so much because timelapse, as you know, generates an enormous amount of data on, on an M, on the individual MBO level, on the individual oocyte and sperm level, the data that goes into SART and other national reporting sites in other country.

is, is limited or none. So that, that data is very independent from, from the argument I made, which is, you need to know that data. And I think that data has driven this process. In our country, we've just looked at like, look how we are doing. We have a national report. And if we look at that national report, yeah, we are slightly better than other countries.

I only, Do the comparison looking at individual embryos, because if you look at it on a patient level, well, some patients will have two embryos, quite a lot still. Most will have now one embryo, which is what has changed in the last 10 years. But it's difficult then to compare. What you really have to do is compare on each embryo that's being transferred, how many led to live births.

How many implanted, how many led to live births. You're going to get a live birth rate per embryo that's transferred. And if you compare that to other dead populations that are out there, I think we are clearly better corrected for confounders and confounder is a factor that affects the outcomes.

Maternal age is the most important confounder, but there are probably hundreds. My colleague of mine called Rusty Poole from Texas has, has published this years ago and he came with more than 200 co founders and he was probably being modest. There are probably more, in other words, those are all factors that affect the outcome.

So, it is a little difficult to look at another country and say, well, this is what you're They're not doing as well. But if we just look at if they report on maternal age groups, we can make the comparison. And that's what we do. Often counties will only compare patients over 40 and lower than 40. If you look at it per age, 35, 36, 37, 38, they compare all age groups.

You see that drop off in panacea rate and an increase in abnormal chromosomes. and aneuploidy. Highly correlated with each other. That's why we have gone to the PGTA route. We, and also the sync. We think we're syncing PGTA because, yeah, you may be, you may be living in a country. So I'm originally from the Netherlands and in the Netherlands, you get three free treatments for everybody.

It's for everybody. You have three, three treatments. That is three egg retrievals. You can have 10 transfers, all included and free. So you could have 12. Okay. Also there, you see a drop off and how patients returning. It's just, it's just, it's striking that not everybody necessarily the pleats, all the embryos that have been frozen.

It's striking. And that may even be, they may, it may even have PGTA. So they know they have no embryos that look nice, that have normal chromosomes and they do not return. And so, therefore, you need to get, you need to get the first shot is the most important thing. The first and the second, the second attempt are the most important.

Some patients react differently to this. I'm, I'm, I'm not trying to generalize. Some patients say, well, no, I'm going to go for this. I will take a look at every embryo that I have and have that transferred one at a time. And if that doesn't work, I'll have another act of retrieval. But that's, that is not the norm.

[00:19:45] Griffin Jones: Do you think that time lapse will become the norm in the United States, that it will be a must have in the next some years that Embryologists will demand it if they if they've gotten a taste of it elsewhere And they then perceive it as the is the standard or do you think it will continue to be an option?

[00:20:06] Dr. Jacques Cohen: That, that is a hard prediction to make. I think, I unfortunately don't have the data saying, well, how many clinics out of the 400 clinics, how many of those have time lapse and use it all the time? 

[00:20:17] Griffin Jones: I'm guessing it's less than 20%, right? And I don't, I don't know. I don't know how much it is, but it's probably, it's maybe 10%, maybe between 10 and 20.

Yeah. That's, that's a, that's a guess. 

[00:20:28] Dr. Jacques Cohen: Yeah. But let's not forget that if we would know those numbers, which we don't, if we would know that number and would know how much it is in the Netherlands, right? So how many, how many time lapse clinics are there in the Netherlands? How many are there in the UK? Well, there are frankly, we don't have the numbers.

We think everybody in Europe is using time lapse, I can assure you they don't. And it's the same for them saying, well, every, every, every patient in the United States gets BGTA. They were saying that about us 20 years ago, and it was only a few percent. Right. And now it's just climbing up to 50%. So it's hard.

So once you know those numbers, it's always striking to see that there's not necessarily the norm and that it's just the frequency. Their frequency is probably higher than ours. But I think, I think you have to, now, now we're driven by large clinic networks. And, and, and so they often look at the bottom line and time lapse is more expensive, not just buying an incubator.

The incubator is just one expense. It's just embryology spent more time analyzing the data that comes unless you have a fully automated process and data analysis, which, which, which involve AI, artificial intelligence, and those packages have been approved in Europe or are used. experimentally and they have not been approved in the United States necessarily.

So the European clinics have much newer versions of their software and AI analyses than we do. We, we still have to do it kind of by hand. I think that may be changing and maybe I'm a few months behind and it was approved, but it's very difficult to get. An IVF related AI or any clinical AI that's based on, on, on machine learning, uh, and neural networks.

It's very difficult. to get those, uh, approved by the FDA, simply because the FDA loves algorithms that are stuck. So in other words, it's the same algorithm that's approved, but if you're changing the algorithm because you have AI feedback, well, then you have an intelligence system. And that they, they haven't gone into that very much in that they're, they're, they're worried about it, I guess.

I, it's hard to tell, but I think that they're worried about it. So. The Europeans have that advantage over us. They have more updated time lapse software than we do. So that is a big difference. 

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[00:24:56] Griffin Jones: Is it that European regulators have accounted for the machine learning feedback in algorithms and they, they have a different criteria for, for algorithms where the FDA? prefers one set algorithm or, or what, uh, or already set algorithms as opposed, as opposed to adjustable algorithms.

Is there a difference in, in how the European regulators look at it? 

[00:25:22] Dr. Jacques Cohen: That, that I don't know. I presume there is. There's one or two countries, there may be more that have said, well, let's see, let's wait and see about this and accept it as it is. I see the UK is one of those, whereas in the U S it's no. No, no, we're interested and we will evaluate this, but we need to know more about it.

So I think the FDA is more conservative than the European regulators, but I don't really have numbers and it may be different from country to country, but I know some countries have said, well, let's, you know, let's look at, let's not panic, which is what we do about AI a lot. We panic and we say, well, this is going to take over from us, so it's going to do.

funny stuff. You know, you don't get checked GPT, you know, but it hallucinates, as they say, well, AI packages in healthcare do that too. And I think, I think you can avoid that because you can have a very solid mechanism that, that catches that. And those AIs are not comparable to the large language model, but I think the Europeans have a more, a little bit more open minded about AI than we are.

[00:26:23] Griffin Jones: As we look into the research pipeline, does it make sense to talk about robotics first or to talk about AI first? 

[00:26:31] Dr. Jacques Cohen: They go hand in hand. Yeah, they go hand in hand. So, so the, the, the time, some of the time lapse incubators, this doesn't apply to all time lapse incubators, but, but some of them have, have, it is a robotic system, right?

You put a little dish inside an opening and then that dish is taken away and goes inside incubator and it's photographed all the time. And AI is applied to it, at least to one or two of them. Or two types of incubators. And, and, and you got basically a reading tells you, well, we recommend that you transfer the following or freeze these four embryos and recommend that you can choose one of this is the best one.

According to us, but it doesn't mean you have to do that, of course, but that, that level is, is very different from what we have and where we are. I hope that answers your question. 

[00:27:19] Griffin Jones: So it, well, they, they go hand in hand and, and whenever there's a chicken and egg question, I remember that I remember a quote that David Sable told me like two or three years ago.

He said the entrepreneur's job is to solve the chicken and the egg. And so our, right now is our, where are we further ahead in your view? Are we further ahead in the development of the robotics or are we further ahead in the development of the AI? Bye. 

[00:27:48] Dr. Jacques Cohen: Okay. So just, yeah, no, very nice. It's a very good point.

We, I see, I think, uh, I think there's a lot of effort in AI and also now a lot of effort in robotics. Robotics started earlier. The first papers are from 2007, 2008. Uh, mostly coming from Montreal from new songs, a program professor, you saw at Toronto university, you know, at, at university of Montreal and, and, and he and his group have been building this up for the last 15 years slowly and more and more interest now, at least five, six efforts in companies that have started up in the last few years, starting different aspects of IVF or studying the entire IVF process and see if all of it can be automated or maybe should you just focus at one particular aspect.

AI is, has had a tremendous interest because robotics. It's on a different economic level, robotics is relatively expensive, whereas AI is very doable. And you can, you can develop nice AI packages for relatively limited amounts of money. And so there are a lot of AI companies, uh, David Sable, you just mentioned him, he and his colleagues calculate that the.

There was a few months ago, there were 35. Uh, I wouldn't be surprised if we're over 40 and also wouldn't be surprised that the researchers missed several of them because they are basically, basically not noticeable. These could be companies or, or clinics or university groups that are not noticeable because they haven't published yet or they haven't really been loud enough as a matter of speaking and they're being loud enough that you know about them.

There could be as many as 50. So that is an explosion and most of them focus on MBO selection. Well, I can tell you that, that, that it's going to end up in the typical civic and valley frequency, 95 percent of them will fail. Maybe higher, but this cannot be sustained, particularly because they're competing with established methods.

The established methods are time lapse, morphology analysis, development rate. If you don't lose time lapse, there are other methods and also PGTA. So you're going, going up against 40 years of IVF and to see that replaced overnight, it's just not going to happen easily. But what is nice about an AI is you can just ignore it.

I think AIs need to be either for free or affordable. It's very affordable. So if PCTA means that you charge, let's say, 100 per MVO, an AI should be a few dollars per MVO. Also free. That's, that's my opinion. Because all of this is just somebody's opinion. An intelligence system that's looked at a lot of data and has come to conclusions that are maybe holding up or not.

We don't, we don't have enough papers yet that it is really making much of a difference. I've been involved in one effort and that seems very interesting. But. That's just one and a couple more that have been published, but you know, the advantage of AI is it's simply to, it has to be simple in terms of installing.

You just download a program and you get an interface to work or your, or your system in the lab and it shouldn't involve hardware. So it's very easy. It's just like an app on a phone, but you need input of data, morphological data. You need Maybe photographic or video data input, but if it's time lapse, then you need that kind of input, more, more hard, more computer hardware needed.

If it's just still pictures, it's very easy to do, and you really literally could do it on your phone. And then you get an opinion, and if you don't like that opinion, as an embryologist or a doctor, It's usually nowadays that, that's a, that's a team decision which I'm able to transfer or which I'm able to solve first.

Then you, then you, you can use an AI. If you don't like it, you got another AI. And if you like them both for different reasons, and you have tested two, why not use them both? Then you have two opinions. That's all it is. It's an opinion and it's an assistant. It's not, this is not what you should necessarily be doing.

You, you're, you're, you're. You're the end point of hundreds of millions of years of evolution. AIs cannot compete with you, but they can do one particular thing, one particular thing very well, particularly if they are based on experience of other clinics and you may be in a small clinic and you could use that experience and that, that, so it is democratizing in a way the solutions that you're building in the IVF lab by making use of an AI or it is an assistant.

So that's. A very big, big difference with robotics, where you have to develop, you have to imitate what an ambiologist is doing, what the lab technician is doing. The lab technicians that we, that, that are nowadays, they have been trained for years and experience counts, and you can just see that in a lab.

You don't see many publications showing. results of embryologist expressed of how they perform in the, in the lab, but it can assure you there are differences. And of course, as a lab manager, a lab director, you try to minimize that, but it's, it's, it's, it's, it's amazing that the robot is basically being put in place, replacing that kind of experience.

I personally think it's doable and I think it's going to happen. The timeline, I'm not sure about. Some of these applications could be a couple of years away. Others may take longer. It's hard to say. I hear all sorts of numbers out there. Some people think it will take a generation or two. Others are saying it's going to be a few years.

The truth, the truth we'll find out later. This is going to happen. There are some procedures that embryologists either don't like doing. Or maybe not so good at, you know, you get tired in the lab. When you do a lot of procedures, you get tired and you have good days and bad days. A robot, if it's well developed and well tested, it doesn't have good base, good days and bad days.

It doesn't bring out what it's experiencing at home into the lab. It doesn't look at this phone and pick up the phone or text. It's not distracted. It's, it's, it's an idiotic system that's very, very focused on one particular task and, and that, and that's how you use it. And then it can be very, very helpful.

We developed the sperm selection AI. I don't see if you, once you have had that, I think that is such a wonderful thing to have. It, it, it actually makes your decision faster and you know, you know, you can use it all the time. But you can ignore it. And that's the beauty of AI, whereas if you have a robot in place, well, you would have to stop the process and go into the robot and take the embryos out, or the eggs out, and interrupt the process.

So a robot has to be very, very well tested before it's implemented on a routine basis. It's a very different process. I, I, I think it, it will literally take hundreds of millions of dollars to develop robotic systems. And it probably, if you add it all up, and once you're done, let's say in five years from now, you add it all up, what all our efforts have been, this could be a billion dollar project, maybe more.

So, so AI, where if you can get data from different clinics, you're in, you're in a good place to develop an AI product that could make, could make a difference. Thanks. 

[00:35:04] Griffin Jones: So, when you say that you think that the AI should be either affordable or, or, or very, very low cost or free, do you mean as, uh, as a pass on to the patient?

And do you mean for as long as it is simply as good as an opinion as, uh, an embryologist or a clinician? 

[00:35:27] Dr. Jacques Cohen: Yeah, it goes both ways, right? So if it's a, if it's an add on cost to the clinic, it often is passed on to the patient and discounted. I think this is on a level that it shouldn't be. I've always been surprised that if you are able.

at some point in time to make maybe a difference with a new technology in terms of success rate, whether that's higher fertilization, whether it means that you can get more embryos to develop by changing things in your culture system. We don't pass that on to our patients directly. But there's sometimes these, these develops, like PGTA is of course a good example because it's so labor intense and costly.

But there are others, like assisted hatching used to be in the past, and, and clinics would charge a fee for something that takes a few minutes. And I don't think, I, I personally never felt comfortable about that. I think that, that is, that's often a decision of administrators, but the practitioners may not feel comfortable about these things.

So, we need to tinker on the, with the culture system, which is still the major. research line that exists, right? We're talking usually about sexy things like robotics and AI, and gametogenesis, artificial syntax, making synthetic sperm and eggs. I mean, those are the big sexy projects out there. Most of our research is about how can we make things better and safer?

And those are spreading tiny little steps and suggestions in the scientific literature. And that's where we focus most of our energy. It goes back to your earlier question, because that's really, that's really improving the culture system is never going to change. We will always think of Mr. Culture system.

That is a research line. That's incredibly important. Big breakthroughs in the last 40 years in that area. But because You know, if you change the culture medium ingredients and test different culture media against each other, and I've been hundreds of those trials, people don't get overwhelmed by that.

The lay people out there, they don't, they don't see that as something they're necessarily interested in, but that's why we got better. The cultures making changes to the culture systems, why we have gotten better over, over the decades. That will, that will not stop. That's not going to stop. That's going to continue.

[00:37:44] Griffin Jones: Will the AI not get to a point where it's better than an opinion, where it's better than the average opinion of the average embryologist and average clinician? Will we not get to a point where the AI has the closest to certainty? 

[00:38:00] Dr. Jacques Cohen: Well, that's a loaded question. It all depends on, on what your end point is.

If your end point is helping an, uh, an ambriologist setting up instruments and timing themselves, uh, you could develop an AI. We have developed an AI that's tracking the ambriologist. And I think there, you're probably going to say at some point, well, this is your guide. It's basically somebody who's keeping the books, right?

It's telling you, well, that those tools are, this tool is not looking good. Get another tool. You know, you need to position this differently. Oh, well, one second. You don't see there's a hole in this zone of Pellucida, you know, their AIs can actually take over and, and, or take over, help you to, in such an extent, you're going to ignore it.

Definitely. The decision AIs, those that are not observing and just helping you, but are making decisions, not necessarily for you, but making decisions like this is the best sperm. This is the best ag. This is the best MBO in their opinion. That's an opinion. Is that going to be equivalent to what you would come up at some point?

Yes, I think it will be. I think it will not only be an equivalent, it will be better than what we have come up with. But this is a development. Is that going to be a year from now? I'll be very surprised. Five or 10 years from now. It's going to be, it's going to be there. And look how long it take, took to get PGTA somewhat accepted in this country.

It took 15 years, maybe longer. With AI, it's going to be in the same timeline. So for every, every clinician, every embryologist to be, to accept that technology will take a long time. Uh, but I have little doubt that it's going to be at least as good as what we do, if not better. 

[00:39:46] Griffin Jones: Are you using it right now in the IVF lab, or do you use it to grade cases?

[00:39:51] Dr. Jacques Cohen: Yeah, I'm not running an IVF lab anymore since, since at least a year. But when you're consulting? Yeah, definitely. Yeah, I definitely, I definitely suggest it. There are AIs you can get for free or for very little. There are some that are charging hundreds of dollars per MBO. I don't understand that. It's a changing algorithm.

And I, I don't understand why it has to be that expensive, certainly wouldn't have cost that much to develop. So, so I think, I think should be for very little or for free. And I, I am consulting people say, well, these should get for you for very little or for free. And you could use several of them. That's, that's my advice.

Don't, don't use one MBO selection AI, but use several. If it's, if it's reasonably priced or for free, then that's what you should do. And you got, you got, you got, and then you can basically keep track of that data. See what you thought as an embryologist, for instance, what did you think should be transferred?

What did the two AIs think? And then you can get some analysis later on. You've done a thousand of those after a year or two years and then analyze that data. See if it has worked for you. Are you just kicking AI out, right? It's just turning over an app, just turning over an app. It's not a big deal. I think, I think a lot of it should be for free.

[00:41:05] Griffin Jones: With regard to robotics, you said that this will end up being a hundreds of millions of dollars, possibly a billion dollar project to fully automate the IVF lab. How far into that billion dollar project are we? 

[00:41:22] Dr. Jacques Cohen: I think we're over 100 million, but they're probably between 100 and 200 million right now. I mean, if you just look at Overture, that's already 150 million, I think, so, so we're probably at a quarter, quarter, quarter billion or 300 million in that ballpark, and I really don't have figures.

You know, that's the amazing thing, really, really hard to find out, but we're already probably 300, 400 million up there. I'm changing the numbers as I speak, but, but, but it's, it's a, it's a guess. I think within a few years we'll be at a billion. That, that's, so that includes all the companies. That doesn't mean that the, that one company that is serious about robotics is spending hundreds of millions of dollars, that you could actually focus into robotics.

And if you only are interested, let's say in, in finding eggs during egg retrieval to automate that process, you're probably looking at procedures that probably could be quite inexpensive to apply. But if you're looking at a fully automated robotic. Existation, which doesn't exist yet, or at least has not been published.

There you're looking at a massive amount of AIs, and you're looking at very intricate, very, very subtle and tested robotics and automation. There you're probably looking at a relatively expensive instrument to develop. So that will cost you many, many millions but yeah, if you look at the total effort, really a billion is not so you know, I'm being pushed back all the time when I say this, but is it really if you're already up to 300 million now, by the end before it's fully automated, which I think will take a while, fully automated will take a while.

Easy to predict it will be. 

[00:42:59] Griffin Jones: And so within that system, what pieces have we established in the last two to four years? And what pieces are still missing? 

[00:43:12] Dr. Jacques Cohen: Okay. So what we have established are preliminary data in most procedures, except for one. And that is tomorrow, the tomorrow system based in New York City.

I'm on the advisory board and I've been associated with them since the early days in 2018. So they have developed two robots that will label MBOs or their little devices that they're held in during the verification process and cryo store all the samples. So cryo storage, which was which has been notorious in terms of mishaps over time.

These, these refrigerators, we call them dealers. These refrigerators can fail as all machines can. And so they, they are under a very harsh and then a very harsh environment and they will fail at some point, but it could take 20, 25 minutes before, before these fail. When that happens, it's a disaster. Also, what happens a lot, it's a lot of errors being made.

Because there are all sorts of good reasons for that. Almost all labs will have errors, at least in communication or errors in, in, uh, in the data processing of individual embryos and eggs. And so it's very common. So we want a more secure method. And RFID chips, which is of course an electronic way of labeling, Each MBO separately.

That, that had to be introduced. And it has been done, and TAMUA uses that technology. And then takes, takes a tube filled with a device that has the MBO stuck to it, that's already fittified, keeps it cold. And then sticks it in a pre programmed place. The advantage for the clinic is that they have immediately a log.

If you tell ambiologists, let's audit our, our units. Doers. Let's order a cryo storage lab. It could be 60 doers. There could be thousands and thousands of patients, MBOs in there. Everybody looks for the exit. All the ambiologists are looking for the exit because it's so much work. 

[00:45:09] Griffin Jones: Yeah. 

[00:45:10] Dr. Jacques Cohen: And you're going to find things you don't like.

And so. Here and all that is, literally, you take your, you take your, your phone, you take your phone, you click on it, you have done your audit. It doesn't matter if there are 10, 000 MBOs there or a million MBOs. It will be a second thing after audit. You know exactly where they are and that they are still there.

That system has been put together by tomorrow. That is a robot that is in place and that's available now. There is, there are two other robots that have been developed. for our field, except for time lapse, which of course is a robotic system. Two other robots which have to do with part of the fitification procedure.

Fitification consists of four or five parts, and one of those has been been available already from Overture in Spain and Genia in Australia. The Genia one is at least 10 years old, but because it only does one in four, of the aspects of the procedure and biologists, including me, frankly, have never been interested.

Why would you have a robot where you do the other three procedures and the robot does the fourth part? I want one that hears the dish, frees this, and I then want the frozen embryos to come out and go in something like a tumoral system also automatically. So I don't have to be worried about it, and I get the data in my EMR.

That's really what I, what I want as an embryologist, because that'd be very, very helpful. Fidification is one of these things where experienced embryologists get very, very good at it. But it's, it takes a while to teach somebody to understand all the little details. details of it, and really start being excellent about it.

And so there, robotics would make a major difference. And Xe would make a major difference in things like egg finding, sperm prep, all of those procedures, yeah, so it would make a difference. 

[00:47:07] Griffin Jones: Are there people working on each of those areas right now? Automating AXE, AXE automating, egg freezing, is that, are we in sort of a race to see which company develops that first?

Or is that in very preliminary stages? 

[00:47:24] Dr. Jacques Cohen: It's right now, if I'm to guess and going by the literature, which is maybe only a couple of dozen papers, it's in preliminary stages, but it's getting closer. I think we'll see entirely a series of robotic systems being published in the next year or two, the first stages of that, before robotics becomes really implemented on a routine basis.

also involving the regulatory aspects that are sometimes needed for that, depending on the situation, depending on the type of robotics. I think you'll see, you'll see that that will take, of course, always a lot longer before something comes in team, but within the next months or years, you're going to, you're going to get papers where people are planning.

I can do X finding, not find all that. And I should find out maybe finding more X than I thought that worked. So, so that those things are going to happen probably sooner than, than. And then later, because there are quite a few efforts worldwide. I mean, I said five or six early on. It may be more than that.

Maybe a lot of, a lot of things, but the, so there are two, there are two types of robotics initiatives, companies that are looking at every aspect. And then there are companies, uh, looking at particular application. I don't know what's the better, best approach, but that's, that's, that seems to be what's, what's going on right now.

[00:48:40] Griffin Jones: How about with regard to non invasive genetic testing, non invasive biopsying of the embryo? And I have to give credit to your colleague, Cynthia Hudson, for planting this idea in my mind, because after her interview, she said, shoot, I wish I Thought and talked more about that and, and so she gets credit for, for putting the, the idea in my mind to ask the question, but how, how close do we are, are, are, are, are there preliminary papers about that or, or are we really far away?

[00:49:12] Dr. Jacques Cohen: Now, I say about preliminary paper, Stephanie, um, what are two approaches if you have an AI that selects embryos based on the development of the embryo and, and use machine vision to analyze embryos, that is kind of non invasive, right? That's non invasive embryo selection. And, and that could be trained on, on, on whether embryos are genetically normal or not.

I mean, I've been involved in an effort, it's a company called IVF 2. 0 based in Mexico. We've developed an AI called Erica and Erica was trained, really only trained on embryos. On a lot of MBLs, looking at whether they were normally, whether they had normal chromosomes or abnormal chromosome counts. So whether they were euploid or aneuploid.

Yes. The data was also provided there, which one of those MVOs would make a pregnancy or not, and which one miscarries or not. But the basic training set was euploidy versus aneuploidy. And so that is a non invasive way of doing PGTA, but probably Cynthia was hinting not at that, but at taking a sample from the culture medium.

Where the blastocyst has been, provided the blastocyst was by itself. And then, and then analyzing that chemically or maybe taking a sample of the fluid that's inside the blastocyst, as you know, the blastocyst is fluid filled and the cells are on the outside. Taking a sample from that. Both of those approaches have been done.

Interesting data. But for me, the most interesting paper is if you find DNA there that comes from ambiose, you could wonder, well, why, why is that? Why did that DNA come there? And the group in Bologna in on the, on the Luca Girodi's leadership in Bologna, Italy, they have found recently and published that if you find DNA, The culture fluid that the chances of fantasy of dose ambose is actually significantly lower than the embryos that do not have DNA in their culture media.

And so embryonic, DNA in the culture media, so that tells you, you may be finding DNA and that may help you what anomaly you're gonna find, but it also means what the DNA is there, that's already not a good sign. The advantage of this finding is that you could just test for DNA and that's very affordable.

Just looking at DNA. Rather than getting information back, you have to confirm it has to be embryonic DNA. Once you confirm that, that's all you need to know. If that's there, that embryo probably should be chosen not up front compared to embryos where you could not find the DNA, the embryonic DNA. So, because why would they lose cells?

Well, that means something's going wrong in that embryo. That means that cells die. or lice, and all the, all the content comes out, including the chromosomes and the DNA. That's why they end up in the medium. It's probably not the best sign that it's there. So in my, in my opinion, that kind of non invasive DNA assessment, chromosome assessment, if you like, has a future.

Particularly if you can just, in an easy way, sample the culture medium, say, in a 15 minute test, there's embryonic DNA there, yes or no, and that has a future. To get details of that embryonic DNA, I think that is far, far short. I would, I would go with the AIs looking for embryo selection based on just data and morphology, PGTA data, and, and choose those AIs, and, and already have a dozen of them.

I would look for those. the answers that those have to offer. That's also non invasive PGTA. So whether it's a very good point, non invasive PGTA, getting rid of biopsy is something that we need to try. We really need to focus on that because biopsy is difficult. It's difficult and it's expensive. 

[00:53:09] Griffin Jones: So, it sounds like getting rid of biopsying is on the preliminary end of the spectrum, on the very preliminary end of the spectrum, whereas it sounds like something more like the robotic labeling of embryos and the cryo storage inventory of tomorrow is on the mature side of the, of the spectrum.

What's in the middle right now? 

[00:53:32] Dr. Jacques Cohen: Well, I think the efforts on ICSI, one of them has been published, the Overture Group in Spain, and MBA Tools Lab in Barcelona. They looked, there was an editorial with that paper, they looked and the editor calculated how many of the steps were actually automated. It was a modest number, but nevertheless, that's never been done before and had, had fantasies.

So this was published. Just a few months ago or half a year ago. And I think that that tells you that there is a lot of work done in that area. There's work done on all aspects. I think on the fertification side, I think there's work done to complete those procedures, not look at one part of the procedure, but the entire set of procedures, and it's the same of all aspects.

So we have done, the field has accomplished making culture, the culture system. Robotic. It has accomplished making the acquired storage systems through tomorrow robotic and, and, and it's, it's, it's looking, it's obviously looking at all the other aspects, which means sperm prep, automation, sperm prep, and, and that, that, that's going forward in strides or making it at least so simple that only involves one or two activities by embryologists on andrology donations.

At finding in the laboratory, there's of course an egg finding or egg retrieval. There's two. There's two efforts going on. It's the surgeon, it's the gynecologist or the, the IE extracting follicular fluid. And then the, that follicular fluid goes through the lab and the embryologist looks through the follicular fluid in very shallow layers, so they decant it into battery dishes and look very quickly for acts.

And sometimes those are hiding, sometimes they sit in blood clots. So it's a bit of an art. It needs to be done in, in a, in a. In a timely fashion, you can't take hours, you need to do this in minutes. So that can be automated, the laboratory part can be automated. I stay away from the clinical part, I think in true course that can be automated too.

But the laboratory part can be automated and you'll probably see the first data sets coming out in the next year. 

[00:55:36] Griffin Jones: How about the systems being developed by Conceivable for automating the IVF lab, where does that fall in the, in the spectrum of preliminary to mature? for listening. 

[00:55:46] Dr. Jacques Cohen: Okay, so Consiglio Rouvas is now 12 months old.

I'm the Chief Scientific Officer. So we are looking at trying to automate all aspects of the IVF procedure. And there's at least one other group out there that's trying to do the same. So we're looking at egg retrieval, sperm preparation, we're looking at, you know, Denudation, the process where you strip cumulus cells away from the eggs before they go to eggsheep.

Automation, the full automation of the entire eggsheep process we're looking at, we're looking at full fertification. We're also looking at automation in, in the embryo culture system because we feel that the culture systems are very expensive. So we want to come up It's culture systems and timelines that are much more affordable.

So we're working on that and we're, we're working on full certification with the tomorrow system at the end to cryo store the MVO. So it is, it is the idea is to do all of these processes and then string them together. 

[00:56:49] Griffin Jones: I feel like I've gotten a really good look into the pipeline today, and you've also made a few points to me that really educated me on why time lapse hasn't been adopted to the level that it has in other countries, why the FDA has not approved it.

Uh, AI algorithms. And so I want to give you the concluding floor. How would you like to conclude about the, the research and development pipeline in the IVF lab? 

[00:57:22] Dr. Jacques Cohen: Well, I think, I think overriding what we do in the United States is the fact that funding is so difficult to come by. The largest funding agency in the world is NIH.

And they found, found, what is it, 60, 60, 70 billion in healthcare research. Thank you very much. Why don't they fund the IVF lab? Since IVF started in the early 1980s in this country, I think the first lab is from 1981, the Norfolk lab. There has not been a single. experiment. A single observational set has been funded by NIH.

There's a moratorium on embryo research since the, since the late 1970s, since 1979. That's now by law. So there cannot be any public money spent on human embryo research. It's outrageous because Everything we try to do in IVF, we have to actually go and do this on patients and spend private monies rather than public money.

And so, yes, we can study IVF and do IVF related research in animal systems. But at the end, if you look, for instance, at chromosomal anomalies, there's no animal system that's helpful. You have to find out in the human. So embryo biopsy studies have been very slow to come by. It's because there's not been any.

NIH funding available. I think we have to frame it like that. People saying, well, this is going very slowly. There is progress each year. If you, if you look at the data analysis and, and two of my colleagues, Alex Bissignano and Mina Alikani and I published a paper in 2012, where we looked at fine combed, uh, um, SAR data and found that there is progression in outcomes per MBO of, of 0.

9 percent a year, year by year. That was only a 10 year analysis and it includes all the clinic, but I've, I've looked since then. And it's going up by 0. 9 percent a year. It's more profound in young patients. In patients younger than 35, it's one and a half percent per year, but it is going up. So in other words, we're doing a lot of good things, but it is very, very slow.

Do we have the patience? Do we have the patience to go, to go this slowly? to where it becomes as good as dentistry, right? We go to a dentist, and you don't go to a dentist hoping that your wood canal is going to work or not. You go to a dentist and expect it to be 100 percent successful. Maybe you got a little infection, but that can be treated.

But you want it to be 100 percent successful. And that's what IVF. We want things to be 100 percent successful. Not 98%, not 80%, or what it is now in some clinics over 60%. No, we want it to be 100%. And we really want that as soon as possible. So I think all this technology that we discussed today will, will, will help us.

Play a role in that process, but it's not only technology driven. It's not only technology driven that we go up by 0. 9 percent per MBO in this country each year in terms of implantation and life births. It's not just technology. It's also communication. So what you do, communicating to the community. Uh, conferences, other webinars.

Training is very important in this country. There has always been a lot of emphasis in training. Our doctors s are trained, that's very unique in the world. In, in other countries is usually usually an OB GYN or, or a GP that becomes an IBF specialist. And could they become good at it? Oh yeah, they could become really good at it, but it's a little bit more tedious to do it that way.

And so. So, I think training also of embryologists has changed a lot over the last 20 years. All of those factors, particularly the communication and the awareness, creating the awareness of all this and having a discussion and comparing our data and comparing our methodology, that is making as much a difference to just saying what's all driven by new technology.

It's not just new technology. But the new technology could be introduced a lot faster if we had NIH funding. And we don't. 

[01:01:33] Griffin Jones: Dr. Jacques Cohen, I look forward to having you back on to look at the updates on the research and development pipeline in the IVF lab. I enjoyed this conversation today. Thank you for coming on the Inside Reproductive Health podcast.

[01:01:46] Dr. Jacques Cohen: It was a pleasure. Pleasure. Thank you, Griffin. 

[01:01:50] Sponsor: This episode was brought to you by Future Fertility, the global leaders in AI powered oocyte quality assessment. Discover the power of magenta reports by Future Fertility. These AI driven reports provide personalized oocyte quality insights to improve treatment planning and counseling for IVF ICSI patients.

Magenta can help you to better identify the root cause of failed cycles and counsel patients on next steps to optimize treatment. Download a sample Magenta report plus four key counseling scenarios and see the difference it makes in patient care. Visit futurefertility.com/ivf. That's futurefertility.com/ivf

Announcer: Today's advertiser helped make the production and delivery of this episode possible for free to you. But the themes expressed by the guests do not necessarily reflect the views of Inside Reproductive Health nor of the advertiser. The advertiser does not have editorial control over the content of this episode and the guest's appearance is not an endorsement of the advertiser.

This has been another episode of the Inside Reproductive Health Podcast. Tune in for a new lineup of episodes premiering in June, where we'll be taking a tour of the C suite with a powerful new series featuring CEOs from some of the largest fertility networks in the world. We can't wait to share these inspiring conversations with you.

Until then, stay informed of the latest fertility news with our weekly digest, delivering curated content straight to your inbox every Thursday. Stay tuned for more updates and thank you for listening to Inside Reproductive Health.

222 More Data Than Any Other IVF Lab? CARE Fertility’s Massive 14 Year Build with Prof. Alison Campbell

DISCLAIMER: Today’s Advertiser helped make the production and delivery of this episode possible, for free, to you! But the themes expressed by the guests do not necessarily reflect the views of Inside Reproductive Health, nor of the Advertiser. The Advertiser does not have editorial control over the content of this episode, and the guest’s appearance is not an endorsement of the Advertiser.


What do embryologists do when they own stake in the company?

If you’re Professor Alison Campbell, Chief Scientific Officer and equity owner at Care Fertility, you’d build massive datasets to train a machine learning system to predict live births.

With Alison we dive into:

  • Their proprietary Caremaps-AI system (Saving 10 weeks of Embryologist time per year)

  • Why CARE is building a machine learning system rather than using AI software already on the market

  • How she pitted 10 of her best embryologists against an AI software she was skeptical of (And who won!)

  • The one AI solution she likes for egg freezing (Why CARE Fertility uses that rather than building their own)

Prof. Alison Campbell
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Transcript

[00:00:00] Prof. Alison Campbell: And it's saving loads of time. So now all of that manual annotation is over. I mean, we just get the machine learning model, press a button and in one to two seconds, it's generated all of that data that's previously taken us half an hour or so for a whole embryos course of development from fertilization to, to embryo cryopreservation.

And then it feeds into the same BLAST6, we call it the six, the six model, the statistical model, and we get a score. And that score relates to the chance of a live birth for that particular embryo. We obviously choose the embryo with the highest score. 

[00:00:38] Sponsor: This episode was brought to you by Future Fertility, the leaders in AI powered oocyte quality assessment.

Discover the power of Violet oocyte assessments by Future Fertility. These AI based reports provide personalized egg quality insights to improve treatment planning and counseling for egg freezing patients. Deliver a superior patient experience and improve satisfaction by empowering your patients with an objective, personalized view of their unique chance of success.

Download a sample Violet report plus a roundup of clinical validation research today to learn the difference this tool makes in patient care. Visit futurefertility. com slash irh. That's futurefertility.com/irh.

Announcer: Today's advertiser helped make the production and delivery of this episode possible for free to you. But the themes expressed by the guests do not necessarily reflect the views of Inside Reproductive Health, nor of the advertiser. The advertiser does not have editorial control over the content of this episode, and the guest's appearance is not an endorsement of the advertiser.

[00:02:00] Griffin Jones: What do embryologists do when they own stake in the company? They do cool stuff like amass massive datasets with huge sample sizes and lots of detail to eventually be able to build a machine learning system that predicts live births. You've met Professor Alison Campbell before. She's been on the program before.

She's the chief scientific officer and an equity owner in Care Fertility, the largest fertility network in the UK and Ireland, now with clinics outside those areas. And Alison talks about Care Maps, their system that they started in 2011. When they started with time lapse imaging that is now in its sixth iteration and powered by artificial intelligence to save 10 weeks of embryologist time per annum to improve success rates to be able to predict live birth and why care fertility decided to put in that work to assemble that massive amount of data, make it safe, put it in one place to find a machine learning partner to do that with all on their own instead of choosing one of the AI systems on the market.

Talks about the value of their data set versus that of cares, the expense of those solutions versus what they were able to do. She talks about an AI solution that she does like called Violet, which is made by a company called Future Fertility and what value she saw Violet brought for their egg freezing patients and why they decided to use that solution instead of make their own.

If you're one of these companies selling into fertility networks, you might pay attention to what made Alison and her team agreeable to even do a pilot with Future Fertility. Why would you take the time of 10 of your best embryologists to see how they stack up against a solution that you're skeptical about in the first place?

Better make it easy for them to do that. I asked Alison if CareFertility will begin to sell CareMaps as a solution to other fertility clinics, IVF labs, and networks throughout the world. I asked her if some of the AI companies listening should just try to build that with them instead of what they might be starting to work on now.

Here's what she has to say about that. And once more, enjoy our interview with Professor Alison Campbell. Professor Campbell, Alison, welcome back to the Inside Reproductive Health podcast. 

[00:04:03] Prof. Alison Campbell: Thank you very much, Griffin. It's lovely to be here again. 

[00:04:06] Griffin Jones: It was probably a year and a half ago that I had you on Somewhere Thereabout.

And your episode was popular because you talked about embryologists owning equity and how Important. That is, I have a feeling some of the thread of that topic might reappear in our conversation today, but I wanted to talk to you about a tool that your practice has been using to great embryos and perhaps for other applications using artificial intelligence and perhaps other technologies it's called care maps.

You're the group that you. Work with and for and own part of his care fertility what I didn't realize though Is that it's been around for a little while? so I and I went with a default assumption that maybe it's a couple years old and he started as Artificial intelligence got more into the Zeke Geist in that 2018 2019 Timespan, but I looked and you had one YouTube video from almost 11 years ago now, and so that means you've been using it for at least that long.

So please lay the foundation of what CareMaps is and when it started. 

[00:05:19] Prof. Alison Campbell: Right. Yeah, no, it's a, it's a beautiful story, I think. So it all began around 2011 when the Embryoscope first came to market. And it actually blew my mind, this device that enabled embryologists to to watch the embryo developing in, in real time, really.

So we set upon a great mission to introduce it into our clinics and to collect data from it, to use it to the max. So, and it wasn't easy. It was a very hard sell because of course there wasn't a lot of data around then. So. I had to try and sell the vision to our chief financial officer. We need to buy this kit, and this is why.

And it, it didn't go down well. It, it, they all thought it was just a toy. It was just a nice to have, but it just made complete sense that if we could get more information, from the developing embryo and the time points that it was reaching each of these subsequent cell stages, that there must be some answers within that information that could help us improve outcomes.

So we, we managed to get one free of charge for a fixed period whilst we did a really rushed evaluation just to make sure that it did do what we expected to do in terms of and functionality and imaging. And then we invested in the first one. And we wanted to get some data fast and we wanted to see if we could predict anything fast.

We're quite competitive and we generally want to be first movers in the field at Care Fertility. So we, we decided to annotate really strictly. So that means once we're, whilst we're looking at these time-lapse videos, the embryologist, using the, the viewer using the software that comes with it. Was recording in great detail everything that they saw so every time The cells divided, we introduced user defined variables very early on as well.

So things that didn't come with the software, we thought, well, that might be interesting. Let's also record as a comment, how the polar bodies, the second polar bodies extruded and little details that didn't come as standard. So we had a strict protocol training program and all of the PGT embryos. that were going through our clinic.

It was started in one clinic at this time. We recorded the ploidy when we got the result back. So then we had about a hundred embryos, and this was our very first publication, and we could predict, or we could classify, the risk of each of those embryos. being aneuploid based on the morphokinetic variables.

So quite simply the embryos that were somewhat delayed had a much higher risk of being aneuploid and we published that and it was the frontispiece on reproductive Biomedicine Online, it was really a well received bit of research and that was the first model that we, we developed. That was CareMap. So MAPS standing for Morphokinetic Algorithms to Predict Success.

[00:08:35] Griffin Jones: These algorithms, did they, were they produced by you also? The hardware is the time lapse imaging. And the software you said that you were doing some things like entering for user entered variables. Is that you all building your own software? Did the hardware come with a software? How, how did those algorithms develop?

[00:08:58] Prof. Alison Campbell: Well, the hardware came with software and a viewer and you could enter your own models. within that software. So the first one was quite simplistic. Now we're on our sixth iteration of ChemApps, which is a logistic regression algorithm. So we've had to work with the device manufacturer, Vitralife, to enable us to implement our own algorithm.

So they've been supportive with that. And then more recently we've introduced the AI element. So that sits outside of the equipment, outside of the device. So we've had six versions of ChemApps getting increasingly complex, getting more and more exciting in terms of what they're predicting. So we started initially predicting the aneuploidy.

And then we went on to clinical pregnancy and now our current models are predicting live birth. 

[00:09:53] Griffin Jones: In that video that you had from 11 years ago, it says that it talks about AI was, were you using machine learning at that time or was AI more of a general blanket term compared to what it means today? 

[00:10:09] Prof. Alison Campbell: Well, I, I didn't really know that AI was on the, on our agenda 11 years ago.

I've not seen that video for a while, but that was probably just, just looking to the future and imagining. So we've only been introduced, introduced AI to our care maps in the last couple of years. So that's yeah. And what the element that's. Being with, with it's involved with machine learning is the annotation.

So that's now completely automated. So I can talk much more about that and how it's won multiple awards. It's a quite a great phenomenon. We're very proud of, of the recent innovations that we've done with AI.

[00:10:45] Griffin Jones: I want to ask about that. So in the beginning it was, it was all manual. So you're, you know, you're entering these criteria, but at the end of the day, one individual is grading.

Each embryo just looking at it and then how do you compare so is the it was there anything like side by side like criteria that was just entered in there so that when you're viewing the embryo you're seeing it against your criteria or um, Um, You're just seeing the embryo and then you have to take it somewhere else to evaluate or take that image and information somewhere else to evaluate it.

[00:11:22] Prof. Alison Campbell: Well, initially we do the manual annotation, which is really laborious and we have been doing that for a decade and absolutely no regrets because that's the high quality data, quality assured, manual annotation that we've used to train the machine learning models. So what we do is we sit at the. machine which looked at the device and we'd annotate every stage and then the software that came with the device would calculate the scores based on the model that we'd entered into the device.

So it's, it's, It's only really the annotation element that was really laborious and took a lot of embryology time. The actual application of the simplistic statistical model was relatively easy. 

[00:12:09] Griffin Jones: And so forgive my ignorance, explain to me what annotation refers to it. Is it the, is it's the, the grading of the embryo?

It's the characteristics of the embryo. It's other notes. It's those, that criteria that you set for the, uh, Self-centered variables. What does annotation refer to? 

[00:12:26] Prof. Alison Campbell: So every five or 10 minutes or so, the time-lapse device is taking an image of the embryo through multiple focal planes. And so this goes on continuously.

Right after ixe, you put the embryos or the virtually inseminated cytes into their time-lapse device, and it's collecting these images. So annotation is when the embryologist sits at the screen of viewer. And reviews all of these images. Like a, it's a time lapse movie. I'm using the software that comes with the same device to click to say now it's two cells.

Now it's three cells. Now I've seen the beginnings of compaction and so on. So it's That annotation is the translation by the embryologist of this image information into, into data really. So it's very important that the embryologists are highly skilled and trained at this. And this is one thing that Carefidelity did really well, I think.

We insisted that we trained people very thoroughly, that we quality assured. their annotations. We didn't say, okay, the most junior member of staff can do all of the annotations. And there are arguments for and against, but I think the fact that we, we did it and we stuck with it for a decade, ensuring that everybody was trained and everyone was performing.

Properly as given us this goldmine of data now that's, that's pretty, really valuable. 

[00:13:56] Griffin Jones: I was going to ask about who is doing the annotation. So a junior embryologist could do some annotations, but then what would have a senior embryologist would be doing other annotations or the lab director would have to have the final grade.

Tell me about the delineation of those responsibilities. 

[00:14:14] Prof. Alison Campbell: Well, there was a competency, there was a training program and competency assessment to make sure that whoever it was, it didn't really matter what level they were, it's are they capable, are they competent at looking at these videos. I could train you to do it, I'd say.

It's not, you don't have to be a scientist to do it, you have to be well trained and you have to be meticulous and you have to believe and understand. Why are you doing it? And I think that's so important because if you, you understand the end game, you will do it properly. And then we'll do spot checks to make sure that we agree with those annotations.

If there's something really ambiguous, which happens with the human embryo, sometimes they, they'll go backwards. You'll see four cells and then two frames later, they've reverted to three cells. Anything peculiar, we would call a colleague over and we can say, look, can you sense this? This sends check this for me, and then we'd have a quality assurance scheme that we established ourselves whereby all the embryologists, all the annotators across the network would annotate the same set of embryos, and then we'd look at the intercorrelation coefficients to make sure that they are correct.

close enough, they're not always going to be identical. Sometimes they're a frame early or late. So then we'd look, well, if you are a frame early or late for that particular variable, so for the start of blastulation, let's say, for example, then does that impact the score, the model score, and therefore does that impact the selection, which embryo you would choose based on the model score.

So it was, it was a very thorough and very complex process, but it's, and it's taken a decade to get to where we are. 

[00:16:00] Griffin Jones: Is it now a requirement for every embryologist in your organization, this assessment for competence and annotation? 

[00:16:07] Prof. Alison Campbell: It's always been a requirement at Carefertility, yeah, every embryologist who's annotating needs to be competent to do that, the same way that 

[00:16:15] Griffin Jones: Does every embryologist annotate or is the workflow segmented in such a way that some embryologists are annotating while others, you know, might be freezing, thawing, etc.?

So does every embryologist annotate? 

[00:16:29] Prof. Alison Campbell: Yeah, everyone who's been trained and is competent can annotate. So if they're on the rota for a particular day to do those, the annotations, then they would do the annotations if they're supposed to do vitrification or end collection. So it's, it's done on a rota type basis.

[00:16:44] Griffin Jones: So now you're on the sixth iteration, machine learning has been introduced to, to now be able to do that annotation. Did that, is that new to the sixth iteration? Did that happen? What iteration did that happen? 

[00:16:56] Prof. Alison Campbell: Yeah, it's new to the sixth. So the sixth iteration is the live birth prediction model. It's the most sophisticated model that we've got we've ever had.

It's built on over 6, 000 transferred blastocysts where we know the live birth outcome. And so we realized we are taking so much time to manually annotate all these videos across the network. So. 15 laboratories in the UK, some in Spain and the US that aren't yet fully set up to do this, but at least at the time, what can we do to really save time to improve reproducibility and objectivity because manual annotation is not perfect.

So let's, let's look at this. Let's get the data together, which was no mean feat. Let's find a third party who have experience in machine learning and See what they can do. So we scoped the project. We did the business case and we found a UK company or their international called BJSS and they had no experience in the fertility sector, but they did have experience with.

Machine learning. And I was quite impressed because they'd done some work with, with airports, where they built models to scan suitcases, to identify smuggled animal skulls and things. So I thought, well, it's image analysis. It's very important. And yeah, they were very impressive. So we worked with them very closely for, took about 18 months, I'd say, from them to the release of the minimum viable products.

And it's. It's saving loads of time, so now all of that manual annotation is over. I mean, we just get the machine learning model, press a button, and in one to two seconds it's generated all of that data that's previously taken us half an hour or so. for a whole embryo's course of development from fertilization to to embryo cryopreservation.

And then it feeds into the same BLAST6, we call it the six model, the statistical model, and we get a score. And that score relates to the chance of a live birth for that particular embryo. We obviously choose the embryo with the highest score. 

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[00:21:31] Griffin Jones: This is all embryo grading. There's no oocyte grading happening with CareMaps? 

[00:21:38] Prof. Alison Campbell: No, not with CareMaps. We are interested, um, we've been talking to Uta Fertility, who I know you, you've been speaking to recently. We talked to them about. Trying to do some research together just to see whether Violet, their, or Magenta, their oSight AI tool could add some benefit to care maps.

I'm, I'm a little bit skeptical that it could, but if we could get some marginal gains to improve predictive power, even subtly, then it's worth exploring. 

[00:22:08] Griffin Jones: So would that be for, like, you would be using their oocyte grading in to improve your embryo grading or you would be using it separately to create something for, for oocyte, for oocyte grading?

[00:22:21] Prof. Alison Campbell: Well, they've already got their own tool to assess the oocytes, which we use for fertility preservation purposes. So if we work, if we thought about putting it together with ChemApps, it would be to see whether The assessment of the oocytes from their system could improve the predictive power for embryo selection in our live birth prediction models.

[00:22:48] Griffin Jones: So I want to talk about this, about building an AI solution versus going with one and, uh, future fertility might sponsor this episode. Somebody else might, Burger King theoretically could, yeah, they don't have any control over what you say. So you can, you can say whatever the heck you want, but I, I, I see some people really see the value in certain AI solutions and then other times.

Yeah, I've, you know, I think I heard Santee, Dr. Mune say, you know, try to build one yourself for a lower cost. The costs also range a lot. Some of them seem really expensive. Some of them don't see, seem as expensive, but it sounds like for in the case of, Embryos, you wanted to build your own as opposed to using one of the solutions that are out there with these, these companies trying to get them implemented into clinics.

Why did you decide to go the build your own route? 

[00:23:45] Prof. Alison Campbell: Well, a few reasons for that. We, we tried the systems on the market at the time, a couple of years ago. With AI, we're talking about the AI aspect and it wasn't good enough for us. It didn't It it wasn't comprehensive enough. We can use a an alternative providers Auto annotation tool, but we weren't getting comprehensive auto annotate.

We weren't getting all the data that we needed to feed our models. There were lots of emissions, lots of inaccuracies, lots of, um, sense checking required. So probably a couple of reasons for that, but the main one may have been that it's trained, they're trained on really heterogeneous data from all over the place.

And I have most confidence in our own data. Our own data was massive. Relatively massive. Arguably, probably the highest quality data set for annotated embryos and largest in the world because of the approach that we took. So why would we use somebody else's tool when our data is stronger, bigger, better?

That was at least the mindset at the time. That might be different now, but we've taken the leap and we're not looking back. 

[00:25:05] Griffin Jones: Was that the defining feature of what made those systems on the market not good enough in your view, the, the quality of the dataset? Were there any other reasons? 

[00:25:16] Prof. Alison Campbell: They were expensive.

I think when we first looked at it, and it may, may still be the same, that it doesn't seem to be a really Clear offering in terms of what you get, the pricing model, is it per click per price per click, or is it a flat fee? I think people are just trying to find their way. They don't have lots of publications to justify the quality and the predictive power of their algorithms or their systems.

So it's still relatively new and you have to make a leap of faith if you're going to do it. And so we decided we'll, we'll do that with our own data and take it from there. But I think we've done a good job. We've won. Multiple awards for this solution. We, Amazon web services did a case study on, on it.

We've won a Royal College of Pathologists achievement award, UK IT tech awards. We've just been nominated for another one I heard this morning. So I think, you know, when we're scrutinized by the data scientists and machine learning experts and tech people, they can see that this has been done really well.

[00:26:28] Griffin Jones: Tech bros are scrutinizing you? Yeah. In marketing, we would call them internet Ian's, but that doesn't mean that they don't have a certain expertise. What do they feel that you're, so you've In your view, the established solutions didn't have a high enough quality data set. What do the machine learning geeks critique your solution for?

[00:26:53] Prof. Alison Campbell: Well, they can just see the information that we give them and we're not letting them under the bonnet to just scrutinize it. So they can scrutinize the way we did the build or the way BGSS, our partner did that build, the size of the data. The, the outputs, the predictive power, because we've used this model now respectively for over 2000 transfers.

with the AI element. It's predicting really well, really, really successfully, accurately. And, and the time saving we've quantified, we estimated it beforehand. And now we've quantified it. We've say we're saving 10 working weeks embryology time per year. So that's, that's a great output as well. So there's so many benefits that we've seen from this.

So yeah, patient attraction has been also. A good one, staff retention. We've had embryologists saying, I could not work anywhere else. I could never go back to manual allocation. I could not live without this system and you know, non professional recognition for our work. We've presented it at conferences and we're still writing up.

We've got a lot of information still to share and to publish, but it's, yeah, it's, it's on track. 

[00:28:08] Griffin Jones: You developed this system, you all at CareFertility developed this system because you weren't satisfied with what was on the market and you felt you got a better data set, a bigger data set, better predictive power.

Now that you have something that you feel is better than what was on the market, are you going to take it to market? Are you, should we expect CareFertility to spin off CareMaps and be selling that to the EVRMAs and the EUGENs and the. the, the inceptions, et cetera, of the world? 

[00:28:43] Prof. Alison Campbell: Well, never say never. It's a possibility.

I would say we've got to consider our priorities. And we've, we've got the data. We've got a lot of expertise in time lapse in this type, this area of machine learning, but we don't have a sales force. We've never done regulatory, got regulatory approvals for our, our products. So this is an in house, developed tool.

So we could use it in our clinics, but we couldn't sell it as it stands without certifications, FDA approval, CE marks, and all of those things. So almost certainly if we did that, we would be looking for a partner to help us get there. 

[00:29:25] Griffin Jones: I wonder if any of the current partners who are thinking, man, this is tough.

This is really hard to sell in, into these clinics. Why don't we just do that? Why don't we just try to, to, to take what HairMaps is doing and then make that our product. And we've got the Salesforce and we've got the venture raising infrastructure. And I think that might happen. 

[00:29:47] Prof. Alison Campbell: That could happen. Yep. I think it could happen.

And yeah, open to conversations. And let's say we, we don't have a Salesforce, but actually. You can probably tell, you know, I'm so passionate about it, it's, I probably, some of our team members would be the best people to sell it if it's, uh, if we were to take it to market because we, we've lived and breathed it for 10 years and we trust it.

[00:30:09] Griffin Jones: I'm sure. 

[00:30:10] Prof. Alison Campbell: And so, it. 

[00:30:13] Griffin Jones: And so maybe future fertility can help with the, with the embryo grading, but, but you're, you're a bit skeptical of that, but they are helping you with oocyte grading for egg freezing. Why go with them in that situation, as opposed to then trying to develop your own oocyte solution. So the embryo grading systems on the market weren't sufficient.

And, but, and so you built your own. didn't go that route for egg freezing. Why not? 

[00:30:46] Prof. Alison Campbell: Well, yeah, we considered it, of course, and, and it may still happen that we, we do our own thing, but we focused on one, one thing at a time and we focused on the embryo selection. We, Caught Future Fertility's Violet through its paces early on, because I was really sceptical.

A static image of an egg to predict outcomes, but saying that, we, we assessed it, got 10 of our expert embryologists to compete against Violet, and it beat us. So yeah, we, it could, it could assess and predict better than we could. Not at a very high rate, because there are so many other variables. Sperms takes a huge part to play in it and lots of other factors, but at least for patients who, who want a bit more information and they say, well, how are my eggs?

Without Violet, we'd give them our best judgment, but it wasn't particularly accurate. And with Violet, they get more information, they get images of their eggs. And so it was a nice to have. So, yeah, I, and generally I'm skeptical of static image assessment because human, human embryo development is a very dynamic process and yeah, there's so many things that can impact it.

So yeah, we just got to focus on what we have the most faith in, I think at any one time and put our efforts into that. 

[00:32:12] Griffin Jones: You were skeptical of the static images, and then you put it against your team of 10 embryologists, and it won, but to do that pilot test, you must, they must have communicated some sort of value to you, or if they didn't, you You just perceived the need, you know, it's tight grading to be that great.

I remember last time we spoke, I asked you how many of these different companies pitch you over the course of the year. And I think it is probably a couple dozen that you said. And then I asked how many in a year do you do any, even like a pilot? Program with, and you said, you know, maybe three, I think it's something along those lines.

So you were talking about one out of every 10 or, or, or something like that, that you're actually piloting. What was it about that pilot that you said, this is worth the time of 10 of my embryologists to, to put them against. 

[00:33:09] Prof. Alison Campbell: Well, it was a relatively easy thing to do. It was a quick pilot. It was on a, an app.

And so we could do it quite quickly, gather information fast. There were nice people. There were also passionate. Dan Neo, particularly when he first knocked on the door, very passionate about his product, made sense for them to have looked into that element. And I wondered why so few people had ever really tried to come up with a tool to.

assess the quality of an oocyte. But yeah, they were there right at the start and simple and effective. It's not going to change the world. It's not highly predictive, but it's better than we can do. So I think that's a positive for patients. 

[00:33:53] Griffin Jones: Do you think that's the future of AI companies in the fertility space, like more segmentation?

Or do you think so that they can find a place where they really can be valuable one and then to make easier pilots? Or do you think that somebody has to win this battle to become the AI solution for, for all, you know, embryo and, you know, site machine learning? I should say all embryo and gamete machine learning.

[00:34:25] Prof. Alison Campbell: Yeah, I think it'll, um, it'll be quite a slow journey. There's a lot of competition, a lot of people trying to get a piece of the pie at the moment. Um, but eventually, I think there'll be just a couple of High quality solutions, which incorporates gametes and embryo assessment. I don't think it's really going to be, we're going to see hundreds of different options.

I think eventually the cream will rise to the top. There'll be collaborations, partnerships, merging of solutions. Cause what we want in the clinic is, is simplicity. We don't want to be moving between systems and causing confusion. We need integration of, of good systems and simple, simple tools. 

[00:35:10] Griffin Jones: Are other networks doing this to your knowledge, developing their own embryo grading, machine learning?

[00:35:18] Prof. Alison Campbell: Not to my knowledge. I don't think at the scale that CareFertility have been moving and developing this, this CareMaps AI, I don't think I, I haven't seen that or heard of that. Now I've spoken to some big group scientific leaders who've said it's so difficult to get the together. So I think, I know it was a huge undertaking for us to get all of that data off all of those servers into one safe place.

And so we were fortunate to have the expertise or have the partnerships to enable us to do that. So that was the first step. And I think that some of the big groups might, will be struggling with that. And also if they didn't embark on the journey like Fertility did, annotating rigorously and religiously, comprehensively.

Then they won't have that data set, but you can accumulate it very quickly. Some of the networks now are enormous, and if they just decided to change tack and do exactly what we've done, it wouldn't take 10 years to do it. 

[00:36:25] Griffin Jones: It's difficult to get the data together. You talked about it was possible for you because you had partners, but they could go out and get the data.

Adequate partners to help them with that. What made it possible for you all to bring that data together? 

[00:36:42] Prof. Alison Campbell: Well, teamwork and shared vision, I'd say it's, uh, but it's 

[00:36:47] Griffin Jones: gotta be something in the shared vision because if they wanted to, they could, they could align the teamwork to it. So there was something about your shared vision that prioritized it in a way that maybe others haven't.

What do you, what was that? Why was this a priority? 

[00:37:01] Prof. Alison Campbell: Well, because we'd already got Care Maps, so we already, we were getting great outcomes for our patients, we were generating revenue, we, we loved the technology, we were getting publications from it, so it was part of our DNA, so it was the next step, really, to bring machine learning into that, to save time, we, we were never going to say goodbye to, to Care Maps, we wanted to keep developing it, we'd done that six times over over the last six years, 10 years.

So it seemed to be the next step. And it's quite possible now, you asked the question, that These guys tapping on our shoulders saying, well, look at our solution. Look at our solution that they catalyzed our actions because we we'd been talking about it. But once we realized that other people are starting to bring tools to the market that can automatically annotate and predict outcomes, we should, we should be.

Lead in the way. We should be doing that. Let's, let's get on with it because time moves quickly and We have the potential to to make all those benefits that I described and particularly saving time for our embryologists It was a huge driver. 

[00:38:11] Griffin Jones: Again, my ignorance are other labs not annotating to this degree of detail?

[00:38:16] Prof. Alison Campbell: I don't believe so. No, they're not. I think we were annotating every single embryo for a very long time and And Some of the clinics will have not annotated at all, or else they'll just annotate the blastocysts, or just the euploids. So, if you do that, you, you're restricting your dataset because you're only annotating the good quality ones.

You've not annotated the ones that have arrested the patients. On the third day at five cells, for example, or degenerated at the more realist stage, we annotated everything. So the data that trained us, we've used the manual annotation data that we've trained the machine learning models is, is really comprehensive.

[00:39:04] Griffin Jones: Did that set out, was the vision for that originally to eventually compile a massive data set? Was that the, the main or only driving reason, or were there other reasons for that level of detail in your annotation? 

[00:39:22] Prof. Alison Campbell: Yeah, no, that was the main reason is because we want to the data and in the data will be the answers.

So unless we collect the data really thoroughly and comprehensively, we're not going to get all the answers that we want to find. 

[00:39:36] Griffin Jones: And so now you're at a point where you're predicting live birth rates. Tell me more about that. 

[00:39:42] Prof. Alison Campbell: Well, when I'm describing it to patients, I'll say, well, we've Transferred embryos in good faith.

We transferred blastocysts in good faith and we've put the ones that have resulted in a baby in one bucket and the data from the ones that haven't, they've been transferred in another. And we've analyzed to see the differences in their morphogenetic values, in their developmental timings and morphological scores to see what the differences are.

And then we've built these predictive models. So we, when we do apply our models, to predict live birth, we get a score and the score one will mean that embryo, it's made of blastocysts because these models are applied to all of the blastocysts, the live birth chance is about five percent, so really low chances.

of live birth, even though we, we can see a blastocyst, which is sometimes seemingly beautiful. And then it just goes up to a score of 10 and the chance of live birth is over 50 percent with that embryo. So of course we choose the highest score and we've used the So we've retrospectively validated these models and now we've prospectively validated the models and they work exactly as predicted.

So we achieve the birth rates or the clinical pregnancy rates just as we predicted because it's, it's so accurate. And when you look at morphology alone, which is the alternative, really a standard practice, you have trophectoderm quality and inner cell mass quality, and you have a stage of expansion. And those variables are nowhere near as predictive of life birth.

They, it's just over flipping a coin. It's not, it's not good enough. And it upset, it upset me a lot over the years working with standard practice that one embryologist would choose one blastocyst from a cohort and another embryologist will choose another. Now with CareMaps, we will choose the same one and we'll choose the best one.

[00:41:45] Griffin Jones: With this level of detail and all of the data that you've assembled, would that even be technically possible without time lapse imaging? 

[00:41:56] Prof. Alison Campbell: No, it wouldn't be possible. 

[00:41:58] Griffin Jones: And so it was 2011 where you first started using time lapse imaging. I would say in the U. S. Probably fewer than 20 percent of clinics are using time lapse imaging in their labs right now.

Maybe it's around there. It sounded like you had to make that case in the beginning, but I want to ask what, what percentage of embryos are PGTA tested in the UK about? 

[00:42:31] Prof. Alison Campbell: It's much lower than the U S I believe it's probably closer to 20%. Whereas in the U S it's It's, it's more than 50%. 

[00:42:38] Griffin Jones: Yeah. I think it might be 60, something like that.

And so are, are these two things either or in many people's view that we either do time lapse or we do PGTA? Is there a reason why it's not both? 

[00:42:53] Prof. Alison Campbell: Well, yeah, I'd say in most people's view, they think right. And embryo selection, is it PGT or is it time lapse? If we're talking about modern or more sophisticated.

embryo selection, but actually there are synergies. between them. And we've shown from our own data that you're, you've got a patient with multiple euploid embryos, then you can apply care maps to distinguish between those euploid embryos. And of course we want the best embryo transferred. So if we've got that technology, then, then we should be using it.

[00:43:27] Griffin Jones: So you had to make the case though for the, for the time lapse imaging back some 12, 13 years ago. And This might tie back into the first conversation we have about embryologists owning equity in the clinic and the network, because I think you said something to the effect of that. They thought it was just kind of a nice toy and you had to convince them of a greater business value.

And I think When I just kind of ask around, I've started every embryologist that comes on the show. I asked them, do you think time lapse imaging is a nice to have or a must have? And it seems like everyone is saying they think it's a must have. And yet we have so many networks that don't have time lapse imaging.

So you had to convince them of that. Of that value. And you also had to have seen the value yourself because you own equity in the company. What was that business case that you had to make to your colleagues and, and to your self and that you feel maybe isn't being made strongly enough? 

[00:44:35] Prof. Alison Campbell: Well, you know, it's a long time ago, but the business case related to it being a differentiator and having the potential to improve outcomes.

So it wasn't um, a rock solid because we didn't know if it would definitely improve outcomes. We didn't know how much, how much it was going to cost us as a whole network if we were going to end up rolling it out across all of the clinics and we hadn't really been certain about what we would charge patients for it and if that was appropriate or not.

So there were lots of discussions. We did invest in it because the hardware is very expensive. We did start to charge patients. Once we'd got confidence in it, we had, we didn't charge for six months while we collected the initial data and we built our preliminary models. So once we had demonstrated that it was going to help us with outcomes and it could predict, predict Ploidy at that stage.

Or risk classify and the patient feedback was really positive. And one of the questions that we asked the patients was how, how, what, what, uh, did you like about the time lapse? What did what, how could you relate to it? What did you feel about it? And the patient feedback was mostly we, it really aided our understanding as to what went on in the laboratory.

And we also asked them, do you think the price. Is appropriate. And the vast majority said yes. So that was really, that was really promising and that helped us invest in further devices and keep rolling it forward and then invest in the statistical analysis and then more recently in the, in the machine learning.

[00:46:20] Griffin Jones: So it was more of a longterm play though, if you're thinking about differentiation that way, because I think if, if you're not looking on a. Five, 10, 12 plus year horizon. Maybe it's more expensive. If you're, if you're looking on a three year horizon, then it's pretty big expense to have for all of those IVF labs, isn't it?

[00:46:43] Prof. Alison Campbell: It's a huge expense, but the return on investment. Didn't take much, it wasn't too long before it came, came back because if you're charging 500 pounds per cycle and the device was 60, 70, 000 pounds at that time and you're getting good uptake and because it's a patient choice, it's an add on. It, it wasn't too difficult to get the money back in order to then buy the next device and so it just kept rolling and so it's been a great success financially, success rate wise.

Staff wise, time savings, efficiencies, and R and D wise. 

[00:47:22] Griffin Jones: It seems to me that in order to meaningfully improve success rates, and in order to have differentiation, people have to have the data. They have to have the data for everything. And so that refers to the tools in the lab that allow you to capture embryo and gamete data refers to, uh, software that allows you to capture clinical data and.

Other inputs and outputs. I don't think people will be able to differentiate without it. We talked about the market possibilities of CareMaps. Maybe somebody listening will say, Hey, why don't we throw in the towel on what we're doing and try to build CareMaps out into a, a side company that could sell into.

Other networks. We talked about the possibility of some of those companies merging as competition thins out and someone emerging as the ultimate AI solution for the IVF lab from a technical perspective, what's on the horizon for care maps. This is how I want to conclude our conversation today. What would we expect from iteration seven?

[00:48:33] Prof. Alison Campbell: Right. Iteration seven would ideally. be device agnostic. It will be cloud based. It will not be tied to one particular time lapse device. It would be accessible to this maybe version. This is the future. This is the dream. It would be nice if we could get it certified and enable other people outside our network to use it and to see, feel the benefits of it.

And for those patients of those. competitor clinics to also feel about the benefits of it. So we'll see. 

[00:49:11] Griffin Jones: When do you work on the next iteration? Does it like, does that work immediately begin as once you've completed an iteration or is you, you work on an iteration for once an iteration is implemented, you wait a little while to see what the needs are.

How does that work? 

[00:49:28] Prof. Alison Campbell: Yeah, we wait a little while. We usually Keep one version going for 18 months, two years before we see like how big is the data set now? Are there any, where, where, where's the area we could tweak and improve? So yeah, it's, we'll be coming up for those port processes soon, but version six is, is working phenomenally well.

[00:49:50] Griffin Jones: And how often do priorities shift in what you expect the next iteration is going to need versus what you end up actually doing? So in other words, going into iteration five, you implement. the fifth iteration and you're thinking this is probably what we're going to need for the sixth iteration. How much does your expectation, your assumption match up with what you end up actually needing for that next iteration?

[00:50:18] Prof. Alison Campbell: That's a really good question. We, I'm not sure so much thought goes into it as you might imagine. Really, it's because the data set is growing. We've got the ultimate, in my opinion, the ultimate outcome measure, which is live birth. So previously we were just reaching for the better outcome measure. So it took time for us to feel confident we've got enough data now to predict live birth because you have to wait, obviously, quite some months to get the live birth data after you've done the annotation, after you've collected the embryo data.

So we'd started with ploidy prediction and we'd moved to clinical pregnancy prediction. And then we got there with the live birth prediction because we had the data, we had the numbers. So I believe we've got it. Got that in terms of outcome measure. It is the best. And people do argue that it might not be this.

There are other variables. Pregnancies can be lost, needs to be a euploid. So we'll see that that mindset could change, but I don't think it will. So going forward, what we're going to be looking to do is to save more time, make the model, uh, better. faster and better and bigger and more accurate, but always I think looking for the, for the live birth is the outcome measure.

So it'll come now, the improvements will be more for user friend, more user friendliness. It's more time savings, and I think now with predictive power. 

[00:51:46] Griffin Jones: I look forward to having you back in another year or two to see the progress that you're making with CareMaps, with the other technologies that you're paying attention to.

This has been yet another fun conversation that I think our audience, especially our lab audience, is going to like to hear. Quite a bit, but I think also the executive leadership is going to appreciate your take two with differentiation and new market opportunities. Professor Alison Campbell, chief scientific officer of care fertility.

Thank you very much for coming back on the program. Thank you very much. 

[00:52:18] Sponsor: This episode was brought to you by future fertility, the leaders in AI powered Oocyte quality assessment. Discover the power of violet Oocyte assessments by future fertility. These AI based reports provide personalized egg quality insights to improve treatment planning and counseling for egg freezing patients.

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Visit futurefertility.com/irh that's futurefertility.com/irh

Announcer: Today's advertiser helped make the production and delivery of this episode possible for free to you. But the themes expressed by the guests do not necessarily reflect the views of Inside Reproductive Health nor of the advertiser. The advertiser does not have editorial control over the content of this episode and the guest's appearance is not an endorsement of the advertiser. Thank you for listening to Inside Reproductive Health.

218 "The Clinic Operating System We've All Been Wanting" with Dr. Mark Amols and Elizabeth Lee

DISCLAIMER: Today’s episode is paid content from our feature sponsor, who helps Inside Reproductive Health to deliver information for free, to you! Here, the Advertiser has editorial control. Feature sponsorship is not an endorsement, and does not necessarily reflect the views of Inside Reproductive Health.


Ever wondered how much your fertility clinic could achieve with just a 5% increase in efficiency?

In this week's episode of Inside Reproductive Health, we explore this question with returning guest Dr. Mark Amols, Medical Director of New Direction Fertility Center, and Elizabeth Lee, VP of Wellnest Fertility.

Join us as we dive into:

  • The impact of your EMR on your clinic's performance

  • Where a 5% efficiency boost can generate 25% overall clinical improvements

  • How enhanced efficiency can unlock patient access to care

  • A brief demo of Embie, spotlighting its clinic-streamlining features


Dr. Mark Amols
LinkedIn

Elizabeth Lee
LinkedIn

Embie Clinic
LinkedIn
Instagram

Transcript

[00:00:00] Dr. Mark Amols: I think one of the reasons that everyone needs to demo this, regardless if you're looking for an EMR or not, it's going to open your eyes to realize that there's more to the EMR than what you've been looking at. You've always looked at the EMR as a system that just tells you the information that you want, but this system actually works with you.

It's a marriage where you're not working against each other, but you're working with each other. 

[00:00:20] Elizabeth Lee: This is really the clinic operating system that we've all been wanting, but never could find. Think about how, if we started to think about clinic operations like this, in this type of succinct, smooth way, think about how many more patients we could help.

[00:00:37] Sponsor: This episode was made possible by our feature sponsor, Embie Clinic. Is your EMR holding you back? Is an Excel sheet your one true source of data? Are you wasting your time with disconnected point solutions? Embie Clinic's unified solution for the clinic and patient provides a single source of truth. Our suite of tools helps you flex and scale your fertility practice from clinical care to the lab, administration, and beyond.

From onboarding to baby in arms, Enby makes sure your patients are informed, Educated and supported every step of the way. Say goodbye to the old and welcome a new standard of care with Embie Clinic. Visit embieclinic.com/irh now to book a demo and take the first step to modernizing your clinic. That's embieclinic.com/irh.

Announcer: Today's episode is paid content from our future sponsor, who helps inside Reproductive Health to deliver information for free to you. Here the advertiser has editorial control. Feature sponsorship is not an endorsement and does not necessarily reflect the views of Inside Reproductive Health.

[00:01:57] Griffin Jones: Could it be that this is the Chosen One? Is this the promise that has been foretold? The Slayer of EMR? The Trident of Triumph? That allows you to finally start getting some meaningful clinical efficiency and stop doing all that junk you hate? I have no idea. I'm not a clinician, remember? That's why you have to check out for yourself and why I brought on two clinicians.

It's worth it. Elizabeth Lee, who's been a fertility clinic nurse for many years and is now the VP of operations at a new fertility clinic network called Wellnest Fertility. And Dr. Mark Amels, who's been on the program a few times now, despite the annoying technical difficulties I've thrown at him more than once.

Thanks Mark. Before we even talk about EMRs, we talk about how a 5 percent efficiency in one area of your clinic can lead to a 25 percent efficiency or greater and have impacts. in every area of the clinic and the lives of the people touched by the clinic. Yes, including patients. Yes, including providers.

Yes, including staff. If you've already decided that you're only going to listen to half or a third of this episode and all you care about is what is this revolutionary EMR slayer, skip to the last third. I think this conversation about compounding efficiency is really valuable because whether it's this solution or another, this is what we've been asking for.

It's the direction that we have to go in. I did a teensy tiny baby demo with Elizabeth in that part of the episode. I can appreciate you're probably going to want something longer form. We're putting those links in with the show notes with this episode, in the places where this episode comes out. Click on that, schedule your demo with Embie, and let me know.

Because I'm not a clinician. Are Elizabeth and Dr. Amos just sugar high on pixie sticks? Or is this the technology that you have been clamoring for years? Your input really matters to me. Please. Let me know what you decide ms. Lee Elizabeth. Welcome to the inside reproductive health podcast Dr. Amos mark.

Welcome back to this darn podcast 

[00:04:01] Elizabeth Lee: Yes, thanks for having us really excited to be here 

[00:04:04] Griffin Jones: There's a particular angle that I want to get in today because of a previous episode that I had recorded where I had a number of REIs from many different parts of the world email me after that episode and I want to get into what that was about.

I first want to broached this concept of thinking about how marginal efficiencies can have a compounding impact and maybe like the efficiencies themselves aren't marginal, but I'm talking like if you make your clinic 5 percent more efficient, if you make it 10 percent more efficient, that There is a compounding benefit to that.

And, and so Mark, you are someone that I think lives it's, this is now your probably third or fourth time on the program. It's at least your third. The first time I had you on was during COVID. It was a live episode. We had over, We had maxed out the zoom limit for the people that could attend. And I was like, people are going to have to do things this way.

I thought that it was going to have to be, I thought it was going to be sooner because I didn't know how many trillions were going to get pumped into the economy. That bought people some time to not be crazy efficient. But now as I, but now where I see things are going, it's okay. The way that Dr. Amos and a handful of others are approaching this.

That's. Just the way it's going to have to be to expand access to care. So what about this idea that increasing efficiency by 5 percent or 10 percent or something, what you might consider small can have a much larger impact.

[00:05:45] Dr. Mark Amols: Yeah, I mean, absolutely. That's how I run my whole business is efficiency, getting rid of the bottlenecks.

I think one of the interesting things about this show is when it comes to this product MD, that is really the center of your entire. Practice, right? So everything goes through it. So when you talk about bottlenecks, even a 5 percent increase in efficiency, if it's at the EMR might actually lead to a 25 percent efficiency because now different departments can talk to each other faster, different things are happening versus like you're saying, if only a 5 percent or 10 percent efficiency occurs on one area, let's say at nursing.

That only helps at that nursing portion. So there's different things that have downstream effects that also make the full clinic more efficient. And so this is what's unique about this system, which I'm excited about. And we've been working with now for a period of time, is its efficiency for the whole clinic, not just one area.

And so most of my focus has always been on individual areas, how to make nursing more efficient, how to make room and patients more efficient, all those little things. I look at everything in time. So as the old adage goes, time is money, I look at everything as time. And so that is one of the efficiencies is time, because the one thing we all don't have is, is more time.

And so we're all working on the same rules there. And so the ones that can find a way to improve the time efficiencies are the ones who are going to come out ahead. 

[00:07:06] Elizabeth Lee: Yeah, it doesn't count. It doesn't take that long to count to a hundred. It really doesn't. And so I want to even posit that 1 percent efficiency gains over time are the reason why people like Mark are able to run his practice the way, uh, that they are.

Mark and I actually know each other really well, and I helped him build a, Getting things really going in terms of being very high volume. And what he and I found was that same thing. It was these little areas that really added up quickly, just the costs. And one of the ways Mark's able to offer the costs he is because he really cares about looking at each line item and saying, okay, these consumables are too expensive.

We don't need to be spending this much money on syringes or something. So I think 1% Little 1 percent gains are all that's needed. And I think people think about it in much bigger chunks and that makes it harder to swallow. 

[00:07:59] Griffin Jones: Why don't we talk a little bit about what it is that you do in helping other clinics?

And I understand you're the VP of ops for a brand new clinic network. And I want to talk a little bit about that because before. We started recording and you said that you and I had met. I didn't remember. And I'm, and I thought I would have, it seems like I would have remembered that because I know who you are.

Like I've heard your name in a lot of different places and people are like, Oh, you got to talk to Elizabeth Lee. Elizabeth Lee is over here doing this. And so I've, I've. I've seen some of the activity that you've had in a lot of different places, and more than one person, it's probably three or four, have directed my attention to you.

What is it that you're up to? 

[00:08:44] Elizabeth Lee: Yeah, thank you for that. That's having started as a little baby IVF nurse with Mark many years ago. It's very humbling that anybody would mention my name. I spent the last year or so really doing consulting and trying to bring this topic that we're talking about, this idea of minimal efficiency gains to create big change.

But working with some big clinic groups, some donor banks, just some different groups that were really looking to make that type of shift. In their thinking to realize some of their goals. And I really spent the last, like I said, year or so working with CEOs really trying to help to shift that mindset and to help see on the ground level or the direct level.

Patient to staff communication level where some of the improvements could be made. That's not an easy thing, right? To say, Oh, you need to make improvements here. No one wants to necessarily hear that. And it's certainly not an easy thing to tell people, but when, you know, this, some of the biggest successes I've saw from organizations was, were ones who said, Bring it on.

How can we change? How can we shift our mindset? But since then, I got offered the opportunity kind of a lifetime, which was to start a clinic from de novo, from scratch entirely. And so that is with wellness fertility, which you're right is a new network of clinics. And we're really looking to bring care to places that there is none.

So Griffin, you talk a lot about it. Mark talks a lot about it. We all three talk a lot about access to care, which I think has become a little bit of a buzzword. Something that I'm looking to tackle with my newest venture at Wellness Fertility is actually looking at how do we really do that? And part of the way we are doing that is we brought on a consultant from Johns Hopkins who actually wrote his PhD on how to improve access to care.

across the U. S. Like he and his wife went through fertility treatment, and he just so happened to be very passionate about this topic. And so he actually helped us do some really deep dive analyses to figure out where to put these clinics, right? Where are these white spaces where there are population densities sufficient enough to support a clinic, but there isn't a clinic there.

And then how do we show up Yeah. To serve communities like that. So that's really what I'm up to now. I thought I was going to just continue talking and geeking out about operational efficiency for the rest of my life. When someone says, Hey, you want to start a brand new clinic? It's hard to say no. 

[00:11:15] Griffin Jones: Yeah.

It turns out if you have good enough ideas and you can communicate them to people specifically enough, somebody is going to say, I want you to do that for us, and you decided to say, yes, Mark. Before you said that. In some areas, a 5 percent efficiency might just be a 5 percent efficiency, but in others, a 5 percent efficiency might actually lead to a 25 percent efficiency.

You mentioned the EMR as an example of that. One, why is that principle the case that a 5 percent efficiency can lead to a 25 percent efficiency? And then why is the area of EMR a good example? 

[00:11:56] Dr. Mark Amols: Cool. Yeah. So like anything, there's a central part, right? So let's think of like a computer, you have a CPU, right?

You can make, you can add on better parts of your computer. And the end of the day, if your CPU is slow, the computer is going to be slow, right? Everything has to go through that portion. And so my example would be like, if I went in and improve, let's say making calendars for a nurse, I might improve that 5%, right?

But it doesn't make me any faster. It doesn't make my front desk any faster. But if I upgrade my CPU. So now the central portion, which everything goes through, improves even just by 5 percent there. It could make the entire clinic increase in productivity because each department now improves. And that was my point.

I think we're a really good example of a clinic that will benefit a lot from a better EMR. I like my EMR. I don't want anyone to think I don't like my EMR. My EMR is not made for IVF. And so one of the issues that we deal with my EMR is that there's a lot of fragmentation. So like anyone who's in the EMR that wasn't made for IVF, there are workarounds you have to make them.

The workarounds usually add time. They usually create a second or third step. And so to become more efficient, you have to get rid of those steps. And one of the things that an EMR would allow me to do if I have one that was made for IVF. is we could skip those steps, get more efficient. And obviously I'll let me talk for themselves.

But one of the things we've been looking at is, and I'm sure if you ask anyone, no one's going to say there's the perfect EMR because just as it exists, because no EMR is made for just one clinics made for a bunch of clinics. But of all the EMRs I've looked at, most of them have one thing that's Not the focus, and that is efficiencies.

That's the one thing you don't see in most EMRs. It's more about documentation, which is important, all the important things you have to have, prescriptions, billing, all that, but they really don't focus on efficiencies. And that's why EMR we've used for a long time is it has been very efficient in certain areas, but it's definitely not efficient in others.

And that's why we're looking at this, and that's why I look at that as the CPU. I look at it as, everything has to go through the EMR, and if that's efficient, it makes everyone else efficient. Does it 

[00:14:09] Griffin Jones: have to be that way? Is the reason why EMRs don't focus on efficiency, it has to do with something that the other outcomes for which they're responsible precludes them from being efficient?

Or is it simply that they have other priorities and efficiency isn't at the top of the list? 

[00:14:29] Elizabeth Lee: Yeah, I don't mind taking that. I think, as Mark said, like his EMR, for example, wasn't made for IV, other EMRs aren't made for a specific clinic, right? And so what happens is, I think, I don't think that any of the EMRs don't necessarily think that efficiency is important, but clinics are having to back their process up.

Into the way the E. M. R. runs. For example, you might have something we're really looking to focus on. It must is trying to tee up our patients so that when they get to the R. E. I. They have all their diagnostic testing done right now. The E. M. R. S. Don't really entirely support that diagnostic front end. Why?

Because not a lot of places do it. I don't know. But at the end of the day, I think what we do have in common is that all E. M. R. S. Serve patients, Right? As different as our clinics can be, we all do the same thing, and that's serve patients in some way. I think that might be what makes Embie special, or have that spark that has caught both of Mark's and I, and my eye, is that it was actually created by a patient.

And it shows, it really does, does show in the flow, in a lot of the headaches that patients experience are, those things are gone. So if there's smooth communication with the clinic, there's ease of scheduling, there's ease of data portability, ease to see your data. You don't have to call the clinic and ask them to release your follicle count to your portal.

It's really a seamless two way communication so that the patient can actually be the center of the care team. I don't know if that answers your question, Griffin, but I think, I don't think it's a matter of not wanting to necessarily focus on efficiency as much as it is that a lot of the EMRs are just really set in the way that they work.

And you can either fit into it or you can do something different. I actually think a little bit different view.

[00:16:24] Dr. Mark Amols: I do think that they are set in their ways. I do think that one of the things MD has in any EMR coming in today is they now have the foresight of what's coming up, right? The one thing we all know is.

There's just not enough positions out there, right? And everyone's looking at different ways to fix that. Some of us look at efficiencies. Other of us look at, we'll just pull in more money and take another doctor. But at the end of the day, there's only so many doctors, so much time. I think Griffin, when you look at what an EMR is, you're right.

There's like a basic portion of EMR that says, okay, I have to be able to do this. I have to be able to do this. What the original EMRs came in with was looking at how do we make things fit better for IBM? For example, is, oh, we can make the partners match up. You can't do that. Most EMRs. So people, oh, that's great.

But again, that's not a very efficient feature. Sure. It helps a little bit. Right. But it doesn't really make you more efficient. It's just, okay, now I don't have to put in there a little note that the husband is this. EMRs used to be able to now track certain lab things that you would have in a lab. But again, doesn't make it efficient.

And when the EMR gets a bunch of people, at the end of the day, this is, these are all businesses. I think the thing we always forget about is everyone's trying to make money, right? We all, we're all just trying to make money. And so when these EMRs get enough customers, they're like, why do we need to make it more efficient?

Everyone's using the program. It's doing the job they need. That it's like a card. It's from A to B. But no one knows that there's more efficiencies there. For example, like a Tesla now, you don't have to drive it anymore, right? It just takes you there. So it's efficient. You didn't even know you needed, but you're like, I really liked this.

Now I can just pop in the location. It takes me there. I think MB is very fortunate. They're coming in at a time when there is this change in our field and this change of meeting efficiencies. And one of the things that, you know, that Elizabeth has, because most of them talk about the selfless and said it, she's extremely smart person.

So just so you know, when I met Elizabeth. I met her and she was, I think you were only a nurse in IVF for what, three months I think it was? I think it was only three months with the clinic you were at. I met her and she had more knowledge and more understanding of fertility in three months than nurses I worked with for, been in a year or two.

And when we met, one of the things that, Really, I got about Elizabeth. We both got each other. We realized that we had to be efficient to make this process work. I told her what the goal of my clinic was, what I wanted to do and the obstacles we're against. And we were coming up with many things. And I'll give you an example of efficiencies that you don't think of.

So back before there was programs like Clara, OMD, all these different kind of text to patient message things. When we first saw it, we were just like anybody else going, Oh, it's extra costs and help us. But then we started thinking about it and we thought about how long do we have to stay on the phone every time we talk to a patient because there's no such thing as a five minute phone call.

Every patient, Oh, it's five minutes. It's 20 minutes. And when Lisbeth and I talked about it, I said, Lisbeth, how long are you on the phone for? I'm on 15, 20 minutes, even for a simple question. And I'm like, wow, if we think about it, we look at the cost and we figured it out, we would save not just a ton of money, but efficiencies.

And so before we had this system, Liz would be there maybe till 5pm or something. We got this message system, and all of a sudden now, Liz would leave at like 3pm because the work was done. We were able to answer 50 patients in an hour. And so the point is, like I said, not everyone realizes, There's a benefit.

Just like we didn't that day. I didn't know there was a benefit, but I'm always looking for it. And that's where I think Envy is so fortunate. And like most companies, they're coming in at the right time. They're coming in at a time when we now are becoming like the primary care, where we have to see more patients in a short amount of time.

And it's the only, not everyone at CCRM can charge a million dollars for a cycle and get away with it. Most of us aren't going to be able to do that. And we're gonna have to do volume. That's how most are going to have to do, especially if it becomes a mandate, when you look at like a Boston IBM, right, they have efficiencies.

And so they're coming in at the right time when efficiencies 

[00:20:26] Elizabeth Lee: are needed. There's really something there that we don't think about our staff burnout levels and what contribution that. Our tech stack or lack thereof is making to those burnout levels. And actually some of the efficiency gains we've talked about earlier, where 1 percent actually may have more of a compounded effect, that's where I think this is because the EMR is every interaction you're having with a patient must be.

Put in the EMR. And so if we're able to create the efficiency gains in that Avenue, then I think our staff become less burned out. They become more engaged. Then they have more to give the patient. Yeah. 

[00:21:08] Griffin Jones: I don't think you can totally bucket efficiency just as this metric for productivity or profitability.

And, but I, and I encourage people to think about it, that your, your Team or your perspective team simply will not use the old fashioned tools over some time because it's asking too much of them. It'd be like asking a landscaper to do an entire football field with just it. a set of shears, right? It's like, we have giant industrial size lawnmowers for a reason.

And once you have them, there isn't any going back to saying, Oh, just use these shears. And, uh, it'll take you about four months, but, uh, have fun with that. It's the same for operations in the clinic too. How did Embie come about though? Which of you two discovered it first?

[00:22:08] Elizabeth Lee: I did actually, I was working with a client while I was doing some consulting work and.

They were getting a presentation over lunch of this new EMR and I was like, okay, blah, blah, blah. And then as soon as I saw it, I was captivated. I was like, wait, what is this and how do I get it? And then very shortly thereafter, I'm texting Mark going, have you seen this thing called Embie? You need to see this new direction.

This would make every impact on new direction. So then that he started to become excited about it at that time. at that juncture. 

[00:22:41] Griffin Jones: Why did you get excited about it first though? 

[00:22:45] Elizabeth Lee: Me, because I could see the drastic difference in, in efficiency, starting with just right upon login, being able to see this sort of bird's eye view of the, the clinical picture.

So Mark will probably start nodding his head here when a patient calls. He and I actually have really good memories as memories go, we remember some strange things, right? But not everybody is that way. And I'm a real believer that systems and processes drive behaviors, right? Things aren't going to happen by accident.

I need to be able to see at a quick glance what the picture is that I'm looking at, who the patient is, who their partner is, what sperm, eggs, uterus, tubes look like. And Embie immediately showed me that in one glance. I didn't even have to try. To find it. And then just as I started to go through it, it just, I could feel the intuitiveness of it.

And at the time when I first saw it, I didn't realize, I didn't know that it was made by essentially from a patient who had gone through eight cycles of IVF and ultimately found success in the cycle where she demanded, not demanded, that's probably too strong of a word, but she insisted on triggering at a different timeframe.

Then her doctor was indicating and why, because she had her own data set from all of her cycles and did some predictive modeling, right? Patients like Mark and I are, and we can remember things patients don't have all don't have that capability, but it just was very clear to me quickly. Not only does it have a beautiful aesthetic, but it's just so intuitive in terms of how to navigate.

And it finally, I found something that could templatize. The things that became very routine, but where mistakes become a big deal, for example, prescriptions. If I order something incorrectly for a patient, everybody's going to be okay. Everybody's going to be safe, but that patient might have spent an extra thousand dollars on a medication that she can't return.

And so Embie also really does a lot of that systematizing. Right? So if systems and processes drive behaviors, then we can build those systems in and Embie really seemed to me to be the first product that I've ever been exposed to that did that, that started to bake some systems into how the clinic should flow.

[00:25:12] Griffin Jones: Will you show me some of this? I want to do a little mini demo because after the previous episode that Embie did sponsor, but it was not a feature sponsor episode. So what that means to the audience is that this, for example, is a feature sponsor episode. If I say Embie’s meh. Embie can ask me to cut that out because it's a feature sponsored episode.

And we tell you the audience that in the disclaimer brought to you by sponsorships do not work that way. They are, we try to match advertisers with relevant topics, but they have no editorial control over the episode. So someone can say something's mass. Um, somebody could refer a competitor, even though that particular advertiser is advertising in that episode.

And after that episode where. And we just had the little mention and an ad in it, there were a number of people that scheduled demos with Embie. And then they emailed me telling me, this thing is awesome. I heard about it on your podcast. And then I booked a demo with them and I'm blown away. And so that kind of gave me the idea for wanting to see some of this today.

And, and I like the idea of having Mark on, because I was saying prior to our conversation that. Dr. Amos is the guy that will try everything and be impressed by not that much of it, is the impression that I have of him. And so, the fact that you're into this makes it intriguing. 

[00:26:45] Elizabeth Lee: That was one reason why I attacked him, because I was like, you know what, he'll bring me down from the clouds.

This is too good to be true. It can't. And so that was really one reason that I wanted to loop the bend, besides just seeing the benefits of his practice, was knowing that he really does have that sense of filtering things out. And I knew he would bring my head back down from the clouds if, if I was over seeing more in it than was actually there.

[00:27:08] Griffin Jones: Will you show me a little bit? 

[00:27:09] Elizabeth Lee: Yeah. Yeah. I would love to. Let's see here. This is your general patient chart. And this is what I was alluding to a moment ago about having all of the relevant data right in front of you. I need to know who this patient is partnered with, right? Cause that makes all the difference.

And then there's just a few key pieces of data that I need to see in order to form the clinical picture. Because Mark, Mark will nod. When you pick up the phone, you have such a brief amount of time to put that picture together before you start losing trust. Because the patient does expect you to remember everything.

And again, Mark and I are like, okay, luckily we remember things pretty well, but not everybody does. And you want to be able to convey trust to your patient that you understand what's going on with his or her picture. And this was really what struck me initially was having this high level overview, but then having the ability to dive under the hood where needed and have all that relevant data.

And Right at my fingertips, but that was a patient chart specifically. This is the clinic dashboard that sort of that practice management hub where. You can also get a bird's eye view of what your day looks like. Oh, I didn't know we had a monitoring today. Who is that? Oh, shoot. Who's the, let's say there's a transfer there and you didn't realize.

There's a lot of reasons that having these, these types of C's are really helpful and then it's just, it's really pretty and that helps, it helps. It's very easy to navigate. If I want to go dive into this patient, I can just double click her. There I go into her chart. If I want to hop on a telehealth with her, I can right there.

Click a telehealth button. I'm not looking for a zoom link. I can immediately. Present the option to just hop on a telehealth. Maybe there's something so you can see within here. I'm not sure if it's ultra mirroring it, but that ability to just right in the moment, hop on a telehealth with a patient. See here, sorry, zoom was covering the ability to exit out.

[00:29:12] Griffin Jones: Sorry, Elizabeth, I want to ask Mark, because I've never worked in a clinic before, right? Explain to me like what Elizabeth has shown us so far. What is the impact that it's having these different features? What is the benefit that it's having on the way your clinic operates? 

[00:29:34] Dr. Mark Amols: Yeah. And I think this is important to understand what area you're looking at.

So for what she was specifically talking about, and this is where I think it's huge is when a patient calls in. And you have to answer a question, even if it's not calling, let's say even just a situation where they send a message through the message system. In most EMRs, you have to go looking through the chart for things.

So maybe you don't see a cycle they did. And so you're talking to the patient and you say, Oh yeah, when you did this, they go, I didn't do that. You're like, Oh, you're right. I'm sorry. And then it makes me sit there and go, what else? 

[00:30:03] Elizabeth Lee: That moment in that moment, you lost a nugget of trust, right? 

[00:30:08] Dr. Mark Amols: Exactly. It's that meme where it says at that moment, you realize you effed up.

That's that moment where you realize. Crap. I just said something wrong. 

[00:30:17] Elizabeth Lee: And that's a lot of stress to put on your staff, right? 

[00:30:20] Griffin Jones: So that brings it up as soon as the person calls or leaves a message. Yes. All their information is right. I just, okay. So now I'm making the connection of what you're talking about, Elizabeth.

If I had the, it's almost like a CRM function, a customer relationship management function. If I had that, I wish that I had that for every time, you know, somebody, Texts or calls me, it's like, Oh gosh, what was the thing that we were talking about? Where's their information? 

[00:30:48] Elizabeth Lee: And I like to think about Embie.

What I think is so beneficial about it is it's really a suite of tools. So instead of having this CRM over here, and this is our telehealth platform, and this is our RCM tool, it's really aggregated all under the same roof because all of those platforms need to share the same data, but typically they don't do a very good job integrating with one another.

And so this is really pulling it, just allowing you to have really. One source of truth, one single source of truth without having to manually redo data. I know for me, one of the big bottlenecks that I saw in clinics was lots of spreadsheets, right? And why? Because it's, as Mark said earlier, it's, I think, I don't think you said a band aid, but it's a workaround, it's a workaround, right?

And Envy really took all of those workarounds and put them into, we don't need a workaround anymore, here's how you'll access that. So here's that overview that I was showing you. One of the things that Mark and I think is really cool about Envy is its ability to visually show data. In a way that really is syntonic with the way we think about it.

So we think about cohorts of follicles and we think about actually the stem sheet will be a little bit better. Um, we think of cohorts of follicles and we think about, um, those developing over time with, in relation to lab levels and just different assessment values. But usually those pieces of data are all in separate places.

Where Embie just brings it all together so that you can see at one glance, once again, this patient started stimulation here. She had her egg collection here. This is how many embryos we reach or eggs we retrieved. Here's how many were fertilized. Of those that fertilized, here was their ongoing culture development.

Uh, here's what was frozen on day five. It's just really this intuitive view of, Oh, what was her estrogen that day? I can hover right over and say, that was her estrogen that day. I don't have to go somewhere else. And look for it. So this was another area that really sold me on the efficiency piece because typically your staff are really left to put all these pieces together and this just puts it all together the right information for the right people in a way that's understandable and in a way that it clicks.

Do you want to say more about what you like about this piece, Mark? Because I know you really like the SIM sheet. 

[00:33:24] Dr. Mark Amols: So I want to make a couple points because I think Griffin was So I want to go stay here, but I want to talk about the last page was, so I was saying how the nurse could look at that page and now they don't have to say something dumb.

They take a look at everything. But as a physician, when you are going through a chart and trying to make a decision, having all that together in one page, your decision making changes. So if I'm thinking something, I look at the anterofocal count and I go, wow, that's a low focal count. I'm really worried about her.

But then I can see the AMH on the same page that says, Oh, our AMH is three. That might change my, my view now that may change what I may do. And so that's having all that in one place makes me more efficient and more accurate. But I want to show you one of the things I just, I was going to tell you, if you asked me, like one of my top things I think so great about this place is the intuitiveness of that.

So when I was at Mayo Clinic, we had a system very similar to this where it had dots and the dots were just a way you could watch everything grow. What's intuitive about that is we're not very good with numbers. Meaning like when someone hears, Oh, 22, 18, 16, 14, in our mind, we hear a cohort. But when we see it, it's so simple.

You look at this page, you go, there's the cohort, there's two that are hired. But they show you the intuitiveness of this program. I don't know if you even know this, but what do you notice about the colors? The purple represent the left ovary, and it's on the left. The blue represent the right ovary, and it's on the right.

They even positioned it anatomically correct. So when you look at it, you have to sit there and go, wait, is that the left or the right? Are those both of them? I You get to make that decision, right? That intuitive, that putting that thought into this is what makes it so great. And every step of the way, that's just how it is.

I love, like I said, to me, that little detail makes it so easy that I don't have to sit there and ask, well, which one's left, which one's right? I know I looked at the screen once on the left and left ones on the right are the right. Those are the type of things that, like I said, speed up the process. 

[00:35:13] Griffin Jones: Yeah, I wanted to ask you about how it normally looks.

And by normally, I mean in most EMRs. Yeah, not like this. Usually it's a number. 

[00:35:23] Elizabeth Lee: Yeah, it's usually a number in a cell, as Mark said. So you'll have the follicular size in a millimeter, and it's just in a cell. And you're usually having to look to see is that left? Is it right? Is it even different? So it's certainly not is not given in the, in a picture that actually just intuitively you can look at and go, okay, I have a really good sense of what happened in this cycle.

Can I show you something else that I think is really cool? It's. Something that the physician has done speaking with the patient, they're going to enter a plan. And that was something that Mark and I, we struggle with sometimes because there was no really great area to communicate a plan within the EMRs.

As the nurse, as the patient calls and reports their cycle day one, that's a, cascade that gets everything flowing. But in, at that moment, at cycle day one on the phone, I can't go find all of the relevant information that I need in a typical system without saying to the patient, let me call you back.

What's really neat about Embie is the physician can enter the plan. And then when the patient calls, I can click one button. That says activate cycle and then right within here I can begin making any adjustments that are needed. Maybe I've heard from Dr. Amels since the patient was seen that maybe they actually need PGTM.

They need something more than we thought or maybe, maybe she's actually going to be using some donor eggs. So there's the ability to craft or to, to fine tune. But then once we save the cycle, now this is another brilliant piece. The system knows. That we do monitorings on specific days relative to the start of the cycle.

And so all of this is baked in to where I can click one button again, systems processes, now I don't have to remember how does Dr. Amos like to do it? Does he like to see them on day five or day six? And then even within here. Being able to make adjustments to the lab orders for that day. Maybe we wouldn't draw a specific lab that day or something like that.

But these are the types of intuitive features that I know really were exciting to me because it was the ability to not have to think through all of this, but have a system in place where I can just let that cascade roll out. 

[00:37:45] Griffin Jones: And so how does this part normally look like? Is there, normally 

[00:37:50] Elizabeth Lee: there isn't, normally this doesn't really exist.

So what you would do is you would build a calendar for the patient somehow. Some people do it in Excel. Some of the EMRs have that ability, but you're going to build a calendar and try to put all of the relevant information that the patient's going to need. And then you have to transmit that calendar to her somehow.

But all of this that you see us doing is all being sent to her app right now. Okay. So this patient can right now see, Oh, my cycle's active. Here's my doses. Here's what I'm doing in the traditional EMR. Now, after the calendar's done, now I have to give the calendar to somebody to schedule all the appointments.

That's super inefficient, right? Who do I hand it to? And what were they doing when I walked up to them? So in the traditional EMR, there really aren't tools like this that allow you to, in a templatized fashion, repeat things based on protocols. Would you agree with that, Mark? 

[00:38:43] Dr. Mark Amols: Yeah, when I first saw this, I thought, did they steal this from our clinic?

Because basically what we do at our clinic is, Elizabeth and I came up with the idea to make all the calendars ahead of time. So when someone's going through IVF, we just pull out the calendar that they're going to be doing. We know the days where I see them, they walk up to the front, they make all the appointments.

And again, it's, it's efficient, but this is more efficient. And the thing that came from a, from a standpoint of, uh, someone who's inputting data, One of the nice things about that too was, I don't know if you noticed that Griffin, you can adjust things even on that page before you hit send. A lot of the programs I've seen, it's pre made and that's how it gets sent out, but there you can actually, even before you hit submit, you can change every little part to it that you want.

Delete things, add things, which now makes it a simple click and you're going. And again, it's just so many steps to remove. 

[00:39:31] Elizabeth Lee: There's just a lot of feeding the staff, the next step, right? Cause like how easy would it be to forget a step to forget to order the meds? This is prompting us. To actually go in and sign the various orders.

Let's say the patient wanted to she was going to go do outside monitoring somewhere These are all of her lab forms that I that are just auto populated The data is transferred over and now one click and this order is gone. I didn't have to write anything I didn't have to pull any data from anywhere else.

So it's really that Continually prompting you. Okay, what to do next and then bringing that information to the patient. Something else that I think was I really liked about this and I would encourage people go to the app store and download the patient app because I really don't think we can overstate That a patient created this and that it really speaks to the needs of patients.

So the educational needs, the mental health and emotional needs of patients. Go look in the app and you'll see that's a little bit of where the secret sauce on the patient side comes, but being able to integrate it across is. To me is a really brilliant piece. I

[00:40:44] Griffin Jones: want to, I want to jump on that for a second, because I've thought there have been apps in the past and maybe, and I think that there's still are that do add a lot of value to the patient in terms of information, in terms of even helping to a certain degree as concierges, but there's always been something missing.

And we've seen app after app come in and either have to change business model. Or they burn through tens of millions of dollars and without ever, like really finding what the business model is. And I've constantly asked, what is it like, how, what needs to happen in order to make this work? And it could be the case that the limit to those apps is that they just never connected to the other side.

Like never really fully integrated with the clinic that there was. It's okay. We can give you this information. And we can. Monitor stuff about your menstrual cycle and maybe even some of your treatment. But then there is a wall once, uh, once we're interacting with the clinic and we have to try and leap over the wall.

This to me seems to be two different sides to the same coin. 

[00:41:54] Elizabeth Lee: You bring about a great point. And it's just, I think It comes down to where is the value ad? And it also depends on what your clinic needs are too. Right? As Mark was saying, we haven't mentioned this, but this tool specifically, something else I thought was really brilliant was the customizability of it.

So the ability, like maybe Mark always likes to see, I don't know, a certain value, and it's not naturally displayed in the app. It's very easy to, to see. To pull that beta in and to customize it for how he works. So, so not only is it just very intuitive and efficient on its own, out of the box. But then you're able to further create refinements to, to make sure it runs the way that, that your practice runs.

We haven't shown this site at all yet, Griffin, but we, and Embie thinks this is really cool. I think this is really cool. Mark and I talked about. Implementing Clara, that use of the bi directional communication tool with the patient, but this bakes it directly into the EMR and it provides that remember that 30, 000 foot overview of contacts.

that matters when I reply to a patient, right? Oh, that's right. No, they're not using a surrogate or, oh, that's right. She's on her 21st cycle or something like that. On our side, on the clinic side, we can see it all aggregated in one thread. So we can see who sent it. And then each of the patient's responses on the patient side, however, they see it as individual conversations.

So they have that ability to send to financial team or send to maybe send Dr. Ams a question. So this is really, I think, quite brilliant in terms of, 

[00:43:37] Griffin Jones: so in a normal pa, in the typical patient portal, how would that look? Would it just be just that? 

[00:43:43] Elizabeth Lee: Usually it's message gets some individual. Yeah, it's usually like an individual message itself.

So if I want to go back and look and see, I may have to click in and out of 20, 30 messages to get the whole thread where I can just scroll up, go, got it. All right. I know what's been said off I go. And yeah, in a traditional EMR, you'd be opening up each individual message from all the different teams.

[00:44:10] Griffin Jones: This is almost like a group. 

[00:44:12] Elizabeth Lee: Yeah, yeah. It's like a WhatsApp group thread. Yeah. So on our side, I can easily say, see, Oh, fantastic. Finance has touched face. The admin has touched face. The counselor has touched face. I can see all of that. And the system allows for tasks to be fired based on the cycle that patient's doing.

So we know every patient is a financial consultant. Every patient needs to sign consent forms, every patient needs to do on and on. And this allows you, when we activate that cycle, it cascades all the tasks out to the right departments to say, okay, we know now she needs a financial consult. We know now she needs these things.

And that too is. I have never seen that in the EMR space. That's always what we're seeing here or what I have traditionally seen people build workarounds for. 

[00:45:06] Griffin Jones: I feel like for so long we've been saying, man, somebody ought to build this. Like somebody ought to I'm not gonna, I'm not a builder. And I think I've known Ravid and Josh for probably a year or so now.

And I don't think that I've fully appreciated what they've done until now. 

[00:45:28] Elizabeth Lee: Griffin. I don't know if you know this, but obviously it was started as a patient app and really looked, they wanted to join forces with the various EMRs and offer this, their platform as an overlay for the patient portal. Right.

Let us give your patients this really intuitive, pretty experience, but none of the EMRs wanted to play ball. And so they looked at each other and said, okay, let's just make it ourselves. And that's exactly what they did. And it's to look at this and to know that this was built less than a year ago and to see the progress with which new changes are coming about.

Something we haven't gotten a chance to mention yet, Griffin, is the AI component. It's not live yet, but it's still in, in, we're still working on it. It actually helped, we created an abstract to submit to Esri to show the data, the accuracy of the data from AI is there. So I'll give you an example. In Embie, we're going to have the ability to click a button and have AI generate the progress note for the day.

[00:46:30] Griffin Jones: You know, who's going to love that mark beyond your team, not having to look at your digital chicken scratch anymore, but your, your family is going to love it. My wife's a physician and she's not in, she's not an RAI, she's not in women's health, but she, it sucks. Like when she's on service week and she has to.

Come back and do notes. And she's just, should I stay at the office and do notes? Should I come back and put the baby down and then do notes? And that's how it would be a lot nicer if that could just go away. And I've been trying to tell her that it will go away someday. And finally, somebody is at least doing it.

[00:47:08] Elizabeth Lee: Well, now she should, now she should just become do be a fertility doctor and she's got a platform. 

[00:47:13] Dr. Mark Amols: I didn't know a step further. And again, this is where I go back to that point. We all think around us. We don't think about everyone else. I'll go a step further. It's not even just about my time. Now, notes are going to be more thorough, right?

I mean, when I read a note and I'm dictating it, I'm not putting every single thing that half the time patients get Dr. Emeril talked about this. I'm like, well, we don't see the note. Cause I can't put out. I got to get home and see my family now. Not only do I save time, but now a complete note is there.

Every little detail is there. And. What AI is going to allow us to do, and which is one of the reasons I'm, be honest, I'm mostly sold on them is because they want to add AI. Is it's going to make things just more efficient, but also it's going to be more thorough. And so I think it's not just about the physician saving time, it's the better quality of notes, the better documentation, the speed of it.

Now more time for the patient, right? So now it's not even just about me. Now I can spend a full hour talking to the patient versus having to spend 30 minutes and the other 30 minutes having to chart. You brought up a point earlier about. Programs. And this is taking everything in. There's a program. I'm not trying to diss on it.

It's a program. I think it was called Sal. I saw it at ASTM one year. I remember what I saw. I was like, wow, this is really pretty. And the reason it came around wasn't because no one could do it. It's because it comes back to that principle again, as an EMR. You have to make decisions. Am I going to make this?

Am I going to make this? People are already using our product. Right? Why do I need to make this? So South came in and said, listen, I'm going to solve a problem. I'm going to give this beautiful, interactive tool between the patient and the clinic. But the problem was, it's a workaround. You're still not going to the EMR.

So what's great about Embie is, they're taking all those things like you said about why don't they put this in a thing and they're putting it into a program, but they're always looking to go ahead. And I do. I think it's perfect. I'm not going to lie and say there's nothing that can't be perfect. But what's interesting is when I talk to them about things and they hear about the efficiencies, they make the changes or they at least think about them.

[00:49:17] Griffin Jones: I want to ask about that because we have, and maybe Dustin will make me cut this part out, but we have seen EMRs in the past come in and to your point, Mark, maybe be more in the time of this digital revolution, starting off with cloud based, starting off with a lot of the digital technology that we have now.

And you've even seen some clinics adopt them, but then some other clinics try to adopt them. And it's just, this doesn't work. There's way too many. Bugs and glitches, and they had to even go back. Imagine how much it sucks to switch in EMR and then having to go back. Yeah. So, so what are the glitches here?

What's the, what are the things that. Aren't ready for primetime. 

[00:50:06] Elizabeth Lee: Yeah. I think that the AI component is really still very much in production. It's not, I wouldn't say today, if somebody were to pick up and be up as their clinic tool, do not expect that's going to be available today. It's something very much still being massaged.

[00:50:20] Griffin Jones: But that's an add on that's being incorporated. What about the core functionality of this? 

[00:50:25] Elizabeth Lee: Is what the biggest opening for opportunity is really to pull their reporting capabilities together. Cause they're all there. But it's just, I think, about finding what are the reporting tools that are going to be really important and then extracting those out.

So I think that's the, in my mind, where I see the biggest area of opportunity is that the ability is there, the framework is there for all of the reporting, which is amazing. But I want to dig a little deeper on how am I going to get the exact reporting that I need to do my best work. 

[00:50:59] Dr. Mark Amols: There, there's a couple of things that when you're looking to look at EMR, right?

You mentioned about adding a product, right? So if you look like Windows, for Windows to be able to keep where it is now, they had to scrap everything and start from scratch. And you're right. A lot of these EMRs might not be able to do this stuff. The way things were written, the way it's coded may not be allow it to.

So this is actually coded in a way that is very HTML5, that's what I'm looking for. It's able to be adaptable a little bit better than some of the EMRs. But. Where the question you were asking is, I think we're the biggest drawback to going to the Amari is the amount of work that's going to it. And I know one of the things they're working on is ways that be almost a turnkey approach where you hit a button and it pulls all your data and goes into it.

And, and that's really the biggest. drawback of going to another EMR is, okay, these are great functions, but are those functions worth the headache of going into a new system? 

[00:51:53] Elizabeth Lee: And I think this is where I like to equate it to a marriage, Mark, right? You've got to be certain, for you, this is such a vital part of your business, that you really need to be certain.

And it is very much like a marriage, and the longer that you are with it, a certain EMR. The scarier it is to think about making the jump and is the data portability there? I think that's what you were speaking to, Mark, is how do I get, how do I not interrupt my clinic operations? And that's actually something I think is quite brilliant about Envy.

And it is, it's just very simple. It integrates with either your Google Calendar, your Outlook. So it's very simple in terms of getting implement, getting implementation up and running. There's not a lot of back end. I think the biggest thing is like customization, right? Where Ravid sits down and she says, okay, give me all of your form.

Give me all of your workflows. Show me all, show me at all. Right. And then she helps to create those little tweaks. 

[00:52:47] Dr. Mark Amols: And it's not AI, right? One thing is you're talking about, everyone can put AI in it, but how you do it matters, right? Just because something has AI doesn't mean it's going to be useful for you.

There is the potential here when you have a company that's really willing to integrate it to make efficiencies, that again, it's not going to be the same as someone saying, Oh yeah, you can get an AI to dictate your notes for you. Like I said, they're looking at it from a different perspective. Which is what makes me excited about them 

[00:53:14] Griffin Jones: to your marriage analogy Elizabeth prior to a marriage You typically go on a few dates a first date might be a demo Do you know the demo that I know reviewed does demos for perspective clinics that do you also do them for?

Perspective clinics, or am I just getting a special treatment right now? 

[00:53:29] Elizabeth Lee: You're just getting, it's you Griffin. But Tiffin, you really, what you have seen is just very much a scratching of the surface. And I do want to make that clear. This is not a full demo. There's so much more to see. And Mervy does an amazing job at walking you through really line by line, the magic of it.

Yeah. You're getting, you're getting me, but Mervy really is happy to give those, those demos. And then the second, third, fourth date is something like what you see here. And that's a sandbox. So Mark's had the ability, as have I, to really dig under the hood and play with it and look at, okay, for my patient flow, how will this work?

How will that work? Seeing what it looks like on the patient's side, right? So we can actually, through the sandbox experience, link, A patient to say Dr. Emil's phone. So he can actually see what the patient is seeing. I think that's really valuable because especially if you, part of your business model is an amazing patient experience.

I think that those kind of the information gleaned from those second and third dates as it were, it's really valuable for them for the overall 

[00:54:30] Griffin Jones: decision. So I'd like to conclude then is why someone should spend the time to do this demo, to go through this demo and probably tying back into the, where the conversation started in the first place of how some efficiencies in particular areas can lead to much greater improvements in many other areas.

And the whole time that you each have been talking about, I'm thinking of. We have to do this as a field, whether it's this particular solution, whether it's other particular types of tech solutions, we have to, there's no other way where there's no other way to get Gen Z's and millennials to, to get even the productivity that earlier generations of docs had been doing much more.

All of the rest of the patient population that needs to be served without these kind of improvements. And, and I'm thinking like, there's just, this has to happen. It's got to happen yesterday. But so I, but I'll let each of you decide of, of what people should be considering about what they test and why this makes sense to think about.

[00:55:42] Dr. Mark Amols: Yeah, I can go first and then Elizabeth and really summarize everything. But I think one of the reasons everyone should do this demo is to realize what they don't have. I think it's going to open their eyes, whether they decide to go with this product or not, it will open their eyes to realize, wow, I didn't realize how much I'm leaving behind of efficiencies, of benefits, just to name a few programs that this would get rid of.

Things like Clara, EngageMD, Salve, All those different things easily gone. Potentially billing. Potentially using AIs right now for dictation. It even gets rid of nurses in some ways. I hate to say that, but I may not need four nurses. Now I may only need three because I don't need someone to double check everything because the system's already double checking it.

So, I think one of the reasons that everyone needs a demo desk, regardless EMR or not. It's going to open your eyes to realize that there's more to DMR than what you've been looking at. You've always looked at DMR as a system that just tells you the information that you want. But this system actually works with you.

It's a marriage where you're not working against each other, but you're working with each other. And the part that we really have not delved into, and I think you just hit on Griffin, is the patient side. We're in a different world now. The old days of sitting down with a patient for an hour or calling them up and set up an appointment, those are gone.

Very few people want to do that anymore. Most people want to point, click, have an appointment, get their information via email. Text versus getting it through the phone. And so, you're right, these changes have to be made. But the problem is, no one realizes what they're missing because they've only seen it through one view.

And that's the view of the old antiquated EMRs. And I don't know if NV is going to be the best forever. There may be something else that comes ahead. But one thing I can tell you right now is, They are definitely far ahead of the other EMRs I've seen, and I work with what I think is one of the more efficient EMRs, and I'm even seeing it have progress over what we're doing.

[00:57:37] Elizabeth Lee: Yeah, for me, Griffin, I think why everyone should go book a demo is because this is really the clinic operating system that we've all been wanting, but never could find. And I think that it is this suite of tools that now finally brings the all of your clinic operations under one tool again, like that single source of truth.

And I think we talked a little bit about it earlier about Access to care being a really important sort of North Star for Mark and I both, I know, Griffin, you talk about access to care a lot, but think about how if we started to think about clinic operations like this and this type of succinct, smooth way.

Think about how many more patients we could help. Right. Think about all of the wasted time, like the massive efficiency tax of just clicking from program to program, just even reorienting yourself. There's a lot of studies that show that is, is very counterproductive. So having that single source of truth, I think allows because we can start to get rid of a lot of that.

That's yeah, that efficiency tax from our current systems. I think 

[00:58:48] Griffin Jones: the financiers would definitely like that topic, Elizabeth, as well as the patient advocates and yeah, and oh, I would love to have you back on in the future for another episode. I also think this is a segue for a topic that I want to have Dr.

Amos back on for which is talking about that top of license. being applied to every different role in the clinic, not just the REI. This has been a pleasure having this conversation with you. Thank you both for coming on the Inside Reproductive Health podcast. Thanks. Thank you. 

[00:59:20] Sponsor: This episode was made possible by our feature sponsor Embie clinic.

Is your EMR holding you back? Is an Excel sheet your one true source of data? Are you wasting your time with disconnected point solutions? Embie Clinic's unified solution for the clinic and patient provides a single source of truth. Our suite of tools helps you flex and scale your fertility practice from clinical care to the lab, administration, and beyond.

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Announcer: Today's episode is paid content from our feature sponsor who helps Inside Reproductive Health to deliver information for free to you. Here, the advertiser has editorial control. Feature sponsorship is not an endorsement and does not necessarily reflect the views of Inside Reproductive Health.

198 What Goes into Building an AI Company in the IVF Space Featuring Paxton Maeder-York

DISCLAIMER: Today’s Advertiser helped make the production and delivery of this episode possible, for free, to you! But the themes expressed by the guests do not necessarily reflect the views of Inside Reproductive Health, nor of the Advertiser. The Advertiser does not have editorial control over the content of this episode, and the guest’s appearance is not an endorsement of the Advertiser.


Whether you’re a fertility doctor looking to make an AI company or a tech entrepreneur entering the fertility field, this week’s episode of Inside Reproductive Health is full of interesting insights.

Paxton Maeder-York. CEO and Founder of Alife Health, breaks down how he started his medical AI company, and walks you through the business and regulatory obstacles required to stay in business.

Tune in to hear Paxton discuss:

  • How an AI company is funded and founded (And If it’s possible to bootstrap without outside investment capital)

  • The unbiased large heterogeneous datasets required to run AI (Not to mention the other companies needed to acquire this data)

  • How he chose his early investors and advisory board (Including former guest Dr. Michael Levy)

  • The monumental difference in data science between 85% good and 99.99%

  • Navigating the high regulatory burdens within the Healthcare Space

  • The criteria for when it’s appropriate for a VC funded company to acquire other companies.


Paxton Maeder-York:
LinkedIn
Alife Health

Transcript

Paxton Maeder-York  00:00

Data sciences, you know, it's not that hard to get to an initial assessment or to, you know, the 85% mark, but that last 10 to 15% of performance is all the difference in the world between, you know, you know, making something that is reliable and safe for patients that's unbiased, and making something that's really more of a, you know, a school project. And I think, I think that's a huge delta that we're gonna continue to see. And I don't just mean within IVF or even healthcare broadly, I think that's a problem that we're gonna see across AI as this whole sector continues to grow. We see it in enterprise we see it self driving cars, we see it everywhere. And so, you know, I think when you're talking about getting to that, you know, 99% or .99 following you know, it requires a really talented team and investment and thoughtful you know, methodical development, and that that does require a capital upfront.

Sponsor  00:55

This episode was brought to you by Embie. To see where your time is going visit embieclinic.com/report. That's embieclinic.com/report. Today's advertiser helped make the production and delivery of this episode possible for free to you. But the themes expressed by the guests do not necessarily reflect the views of Inside Reproductive Health, nor of the advertiser, the advertiser does not have editorial control over the content of this episode, and the guests appearance is not an endorsement of the advertiser.

Griffin Jones  01:33

Building an AI company in the fertility space, many of you have business backgrounds, many of you have medical backgrounds. What about bio mechanical engineering? What about surgical robotics? What about data science? Today, my guest is CEO Paxton Meader-York, I don't delve much into his company a life for what they do for the fertility field. Instead, I tried to give you an idea about how an AI company is funded, founded and managed from the start. We start with Paxton leaving Harvard with a degree in Biomechanical Engineering cutting his teeth in the Silicon Valley ecosystem working in surgical robotics going back to Cambridge getting his data science master's and his MBA back to Silicon Valley, how he chose some of the people on his advisory board and early investors including Dr. Michael Levy of Shady Grove and Dr. Allen Copperman of RMA of New York packs and talks about the investors that led their Series A round and their seed round. I asked Paxton if it's even possible to build an AI company bootstrapped no investment. He talks about those barriers, including the unbiased large, heterogeneous datasets that are required, and consequently, the companies that are usually required to partner with to get those datasets. He talks about the high regulatory burden, especially in healthcare, and the monumental difference in data science between 85% good and 99.99%. I tried to get criteria from Paxton why they haven't acquired a company yet, because I'm trying to get criteria for you of when it's too early for new VC funded companies to go off acquiring other companies. Finally, I get Paxton to talk a little bit about their tech stack, their org structure and their team, software product and so on. If you're a fertility doctor looking to build an AI company or a tech entrepreneur entering the fertility field, I hope you enjoy this founder story with Paxton Meader-York. Mr. Meader-York, Paxton, welcome to the Inside Reproductive Health podcast.

Paxton Maeder-York  03:18

Thank you so much for having me, Griffin, it's great to see you.

Griffin Jones  03:21

I look forward to talking with you. I've had a couple founders on recently, Dr. Brian Levine was one of them and that was a very popular episode. Got to go into the mechanics of how he started his company, I want to go into the mechanics of how you started your company. And I want to, there might be some things, likfe funding and structure that in some of the circles you run with might be elementary, but not as elementary to some of the people that want to start companies in the fertility field. And so let's maybe just start with how your company started. We can we can talk about the idea and the genesis, and then I'll really want to get into the mechanics.

Paxton Maeder-York  04:03

Absolutely. And thank you again for having me on. It's really great to be here. So yeah, I'll start with maybe a little bit of background about myself. So I've been passionate and in really engaged with medical technology for a long time now really started for me back in middle school where I was watching surgeries at MGH and doing robotics camps at MIT. I ended up studying biomedical engineering at Harvard undergrad, really focusing in surgical robotics, and then working at a company called Oris health out in the Bay Area that was focused on lung cancer systems. So I had lost several grandparents to lung cancer. It was a really important mission to me, and really got to cut my teeth in the Silicon Valley startup ecosystem. When that company got acquired by Johnson and Johnson, I went back and did a master's in data science as well as my MBA back at Harvard, and really became passionate around the opportunities for artificial intelligence and advanced analytics, more broadly across health care. My little brother's actually an IVF baby. And so infertility care has always been something that's been incredibly important to me both personally, and as we see the growing trends across the population, it's only of increasing importance to many folks. And so started the company about three years ago really with the mission of trying to bring modern data science techniques and personalized medicine to the forefront of the IVF sector.

Griffin Jones  05:29

So you're at Harvard for undergrad, and that's where you got your degree in surgical robotics?

Paxton Maeder-York  05:34

Yeah, so biomedical engineering undergrad, and then grad school was both masters and data science and then an MBA.

Griffin Jones  05:40

Okay, so biomedical engineering, and then that's what brought you out to Silicon Valley. And I'm sorry, you may have said it, and I may have missed it, were you the the founder of that company that you went to work for in Silicon Valley, or you're working for somebody else at that time?

Paxton Maeder-York  05:54

I was working for somebody else, and really was hoping to learn a lot from a very experienced CEO, Fred Moll, who founded that company actually founded Intuitive Surgical, which is the preeminent preeminent system out there in the robotic surgery space really pioneered the sector. And so you know, learned a ton from working with him and the other amazing folks there, actually, a couple of those I worked with at Oris came over and are now running a lot of the A Life team. So certainly was was an incredible experience for me early on in my career.

Griffin Jones  06:26

So you could have stayed and then worked for a different Fred, and a couple of Fred's and stead of going back to the east coast to get your advanced degree at Harvard. Why, why go back? Why go for the advanced degree as opposed to staying in the Silicon Valley ecosystem that you cut your teeth in?

Paxton Maeder-York  06:48

So you know, I think there are a variety of reasons for it. You know, my, my undergraduate focus was really in bio mechanical engineering, so medical device. And, you know, I got to learn a lot about the complexity of bringing robotic systems and complex medical devices to market, both from a development standpoint and a commercialization standpoint. But I've always been fascinated around data science, and really, its propensity to answer big questions, right? Whenever we think about asking a question whether, you know, it's in politics, or healthcare or any other sector, right, I think, you know, everybody turns to Google and looks at, you know, large scale studies, and really everyone's, you know, looking for data to answer that question. And so becoming more proficient at data analytics, understanding how to use modern data science, especially reinforced with the incredible computational power we have at our fingertips today was just an area I was super passionate about. And on top of that, you know, I always known I wanted to be a leader and hopefully found a company someday. And so by working and getting my MBA as well, it gave me a lot of context on the broader economy, how companies scale, and also hopefully, will allow us to continue to grow into the long term vision that we set out for at Alife. 

Griffin Jones  08:10

But what was it about either Harvard at that time, or the degree itself where you felt like you would get that leadership background more through an MBA and more of the data science understanding from an advanced degree as opposed to working for a couple other biomed startups or a few other, even maybe even more mature companies, out there in the in the tech sector? 

Paxton Maeder-York  08:38

Yeah. So I mean, I think it's a couple of things. I mean, one as an engineer, and I really consider myself as an engineer, first and foremost, you know, I always want to understand as much as I can about the technology before going out and, and building it either with a team or on my own. And so I certainly felt like the the advanced mathematics I was taking in my master's program, and also just really diving in and understanding how this recent kind of trend of artificial intelligence, I know it's a topic that has been talked about since the 80s, if not earlier, but a lot of the really exciting work that's happening in AI is really started in 2017, with a lot of the image based pattern recognition work, AlexNet, and so forth. And and then on top of that, on the MBA side, you know, I worked at Oris, got an incredible kind of mini degree from from that experience, I did spend a summer working with Google X. So got, got to scratch the itch and see what was going on inside of that black box. But with the MBA really gave me was the opportunity to look at hundreds of different businesses and all these different contexts and that type of pattern recognition similar to what we deploy on the actual medical technology side, you know, I think is really valuable as a young person as a leader and as someone who's continuing to try to strive to scale businesses and of course, deliver huge value to both clinicians and patients in the long run.

Griffin Jones  10:02

So I don't think this is degressing too much, I think this might be at the crux of why you went back versus why one might not go back to get that more advanced education. And I think of, there's a common adage that says, You don't have to be the expert in a given field. And they'll they'll cite Henry Ford, and they'll say, you know, Henry Ford was not a mechanical engineer, he didn't build cars himself, he, but yeah, but he knew a ton about cars. And, and I just don't believe that you can't have a certain ground level of understanding in a subject and then build a business out no matter how good you are as a, as a manager of people, as a capitalist in raising money, that you have to have some type of, you have to have some type of background. And for you building a tech company, I think what is, what would you consider the minimum level of background to know that you're not being fleeced? Or that you can, even if you're not being worried about being fleeced by people that work for you, that you can sufficiently instruct to them and delegate to outcome? So what do you think the basement is for that? Or where have you found yourself using your degree or to be able to, to use it to for the vision of the company?

Paxton Maeder-York  11:31

Well, I certainly wouldn't say that these types of degrees are required for anyone trying to start a business. And of course, a lot of the people listening to this podcast, you know, are extremely, you know, proficient, either in their field, a lot of people have PhDs or MDs, I think, you know, it's, it's a tough couple of different components. You know, one, obviously, the nature of the business, I think, is important, right. And, you know, if if there are many companies out there, where the founder may be technical, or may have a purely sales background, and those types of leaders can can bring enormous value to the organization, I think a lot of it does have to do with kind of the mindset of the leadership and how well you're able to accumulate a team of experts in those different domains and fit the pieces to the puzzle together. You know, having said that, I think if you're going out and trying to do something extremely technical, and also something that has, you know, pretty substantial ramifications for your end customer base like we do, in infertility. You know, I think at that point, it's, it's always valuable to have a technical proficiency in that type of technology. And so, you know, it was it was my approach, and it may not have necessarily been the one that is required for everyone. But I certainly wanted to have as much know how in medical technology development and all the regulation and quality management system and you know, kind of the domain level expertise in that having done that in the surgical robotics space, and then combine that with technical know how around data science so that we can look at these problems, and I can contribute, and also, hopefully recruit an incredible team of data scientists and AI experts to this specific application. Which, personally, I think is an incredible application of this type of technology. I think there's so much opportunity for advanced analytics across healthcare, but specifically, within IVF. Just to help support bringing personalized medicine and helping clinicians deliver the best care they possibly can, whether that's digitalising, the embryology workflow, helping to capture image and images and, you know, kind of manage, manage expectations on that side or, you know, helping to select the optimal ovarian stimulation protocol and when to trigger, which is another component of what we build at Alife. So, you know, I think the the short of it is, there is no basement, if that, if that makes sense. But I think, you know, certainly in this arena, I wanted to feel as prepared as humanly possible before I strove out and tried to build the company on my own to go and tackle some of these problems.

Griffin Jones  14:00

And did you strive out right after you got your MBA and your masters in data science? Or did you go back work for somebody else? And then that came later?

Paxton Maeder-York  14:11

No, I strove out right after my graduation. So actually, the application of using AI and computer vision on embryo analysis was kind of a the initial project and something I worked on as my master's capstone thesis. And then that spun out into the company. And then of course, you know, when you start a company, one of the great pieces of advice I got early on in my career from a close advisor was, as soon as you kind of have the pieces in the toolbox that you need, and you have an idea, you go off and do it and you start pulling on the thread. And of course, as you pull on the thread, and you start working on the problem, and you work with customers, and you learn more about the space and you build an advisory board and you ask what types of problems clinicians or patients are seeing, you learn more and more. And so when you look at the genesis of Alife and how much we're doing today relative to the initial idea, a lot of that has expanded over the last three years, and transparently a lot of those amazing technologies or product ideas didn't come from me. The holistic vision came from me of we're going to head in this direction and built incredible products and use AI to help support people who want to start, continue, or finalize their families, but great ideas come from anywhere. And that's really where, you know, bringing an amazing group of people together and working collaboratively, I think personally results in the in the best outcomes.

Griffin Jones  15:35

So you start working on it, at what point did you build the advisory board? Did you build your advisory board before you started raising money?

Paxton Maeder-York  15:41

I did. So you know, when I first started out, I kind of had this idea, I started talking to a few investors and immediately started talking to many different, you know, top doctors in the space, either through you know, connections or cold emails, there's a whole component of this, that is just straight hustle. And you know, over time you build rapport. And you know, some of the incredible folks, Michael Levy, for example, at Shady Grove, now US Fertility, was one of the first folks that I was lucky enough to get to work with. And then as you know, you kind of continue to build reputation in this space, more and more people and top clinicians got excited by both our team, how we were approaching the problem, how we worked on these types of issues together and integrated our clinical advisors feedback. And so our clinical advisory board just has continued to grow. And the whole team, which is now you know, over 28 folks strong, is constantly looking for feedback testing, working with those doc's to run studies to validate our algorithms. It's kind of a constant approach. And so I think that advisory board has been an incredible asset for the company, and we're super grateful to have all of their support.

Sponsor  16:53

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Griffin Jones  18:03

How do you manage the interests of different people either on the advisory board or some of those earlier folks that you're working on the problem and consequently the product with, so Michael Levy at Shady Grove is a very big center and group of centers. And as big as Shady Grove and US Fertility are they're not the entirety of the market. And any startup faces a challenge where they can they can fall into scope and create too much. So how did you how did you balance that, especially with that particular this is a really big center, you could build something that's just for them and assume that it's applicable to everyone. But there's a wide variance in workflows of clinics of all kinds. So how did you balance the needs of maybe this one, two, three, four people that we're working with now in this moment versus what's really going to be scalable for a business going forward?

Paxton Maeder-York  19:05

It's difficult. And I think, you know, this is a pitfall that a lot of people, you know, fall into right is how do you avoid just building a tailored solution to a single customer? And you know, while Dr. Levy was, you know, one of the first people I spoke to just through a connection right at the beginning of the company before I even raised. Very quickly, we built out a much larger advisory board, Alan Copperman from RMA New York who has been really involved with our story, a number of others. And then there's there's just a really thorough playbook that you follow of having a lot of discovery conversations, you know, going to clinics, seeing how they operate, finding those different you know, kind of similarities and differences. And you kind of look for the overlap in the venn diagram where this is a consistent problem across practices. This is something that the technology can you know meaningfully make an impact on. And it is different, you know, a lot of companies in the medical technology space, you know, pick one chief medical officer, for example, and bring them on. And a lot of the product development is done in relation to that individual. And one of the things I've learned in surgical robotics is exactly what you're pointing to, which is that different folks, different clinicians have very different perspectives on you know, what's important to look at. And, as you said, different clinics operate differently. And so, you know, we kind of went with this more broad approach of instead of having a single voice, let's get as many as we can have the top folks in the space, and that is, you know, both, you know, horizontally and laterally across clinics and vertically within those clinics. So, talking to frontline, you know, embryologist, junior embryologist, talking to clinic admin staff, talking to nurses, you know, talking to lab directors, it's really the entire encompassing of the field. And of course, we've interviewed hundreds and hundreds of patients at this point, as we've built free patient products, and also worked to figure out how we're going to bring value with the AI solutions that are going into the clinic. So it's, it's not really a crowdsource model, but it's almost kind of like that. And then, you know, in terms of how to how to really solidify what you're building, I credit our incredible product team, and especially Melissa Teron, who's our chief operating officer, for doing a lot in that that area, there are certainly playbooks you can follow and best practices and you know, modern product development and things like IDEO, you know, really paved the way for some of those things. And the Stanford design school has got a lot of incredible resources. But, you know, it's definitely an art that in terms of figuring out where the opportunity is, and how do you shape the technology to best fill that need. 

Griffin Jones  21:50

When you are getting your MBA, where there are different schools of thought about how you should approach fundraising, that you should phase it in this order, or you should try to get more in in an earlier phase or a later phase, or where there are different schools of thought, and how did you pick the approach that you ended up going with?

Paxton Maeder-York  22:09

You know, there's not only different schools of thought, within, you know, business schools, there's different schools of thought within the venture community. And then there's different schools of thought founder to founder. And that was something that I learned over the course of, you know, the first year and a half or so. And I'm lucky in that I have a number of friends who have also started companies at various stages. But what you learn pretty quickly is that the approaches that other people take around fundraising may or may not necessarily work for you. Now, there are obviously a variety of different types of capital sources out there, you can bootstrap a startup, you can look to private equity, or traditional LBO, you can do entrepreneurship through acquisition, you know, and then there's more of the traditional venture route, which is the route that Silicon Valley is known for, and the route that Alife has taken. But I think what's what's interesting is that, you know, fundraising and figuring out who the right partners are, for the long term, because as you know, as soon as you bring on an investor, and they've put significant amounts of money behind your vision, you're going to be working with them for a long time, they are invested in your story. I got really lucky that I found Deena Shakir, who lead our seed, and lead our Series A and she's been one of our number one advocates for the business since day one. She's been absolutely incredible, through and through. But I think that it's there's so much that goes into fundraising that is beyond just kind of the hype and the FOMO, and pitching. A lot of it in my perspective is about finding that right fit. And who is someone that is going to work with you in the long run? Very similar, I would, I would argue to creating a leadership team. I think a lot about my board the same as I do, developing my internal leaders and how different skill sets are gonna complement each other. So I think every founder is a little different about how they approach that problem. But for me, you know, it was it was a lot of conversations. And I was very fortunate to find some incredible folks, Rebecca Kaden at Union Square Ventures is another one who came on at Series A who just, you know, clearly understands and is passionate about the long term vision of the company. And, you know, I think it's really important to find those folks as early on as you can when you're going out and building something important.

Griffin Jones  24:23

What was it about Deena and Rebecca that made them a good fit?

Paxton Maeder-York  24:27

You know, there's, there's certainly, you know, kind of the more traditional, you know, filters that you can apply, right? Coming from great firms, you know, very sharp investors, certainly asking great questions, bring resources to the table, not just capital but also in terms of advice and network and, you know, you know, other kind of intangible assets. But I think you know, even more so than that, it really is almost a personal decision too. Who do you think are going to be a great fit for your company, the culture you're trying to build? Who, you know, is going to be the right fit for you as a founder? And who, you know, who you want to work with and you know, are ultimately going to be able to, A, keep you accountable, but B, when you need support from the board level or from your investors, or we're going to represent you either in the media or to, you know, follow on investors in later rounds, it is, they say that VC and raising capital is a lot like dating. And I certainly think that that's true. It's, it's, it's, you know, there's things on paper that make it important. And then there's kind of an intangible personality fit that I think is so crucial to get right when you're out fundraising.

Griffin Jones  25:36

Did you have relationships with either or both of them before you went to raise money? Did you meet them during the process?

Paxton Maeder-York  25:43

So I really met both of them during the process. So Deena works at Lux capital, which is an incredible firm, and probably the best deep tech investor VC that I know of, and they had invested in Oris, the company I worked at after undergrad. I had not met Deena during that experience. But you know, when I started Alife and was starting to tiptoe around the capital side of the business, Peter Hébert, one of the founders of Lux, put me in touch with Deena and Peter's a genius, and could tell that Deena and I were going to be a great fit. And then Deena and I spent months and months getting to know each other before, you know, we kind of solidified the relationship culminating in our seed round. And, you know, I really cherish that time. I think it was so valuable that Deena and I got to spend so much time together up front, it's deepened our partnership. And, you know, I think it's, it's ultimately, you know, I consider her you know, as a co founder of the business in a lot of ways. And then Rebecca Anaergia who is from Mavron, who's also incredible, I really got to know a lot closer during the Series A round. And that was a faster kind of, you know, relationship building period, of course, we're continuing to get to know each other and work closely together, every, you know, you know, board meeting and in between and our monthly calls and working through, you know, challenges and exciting milestones for the company, it's constant. But I think similarly, there's, there's just kind of a great fit personality wise, and also in terms of their passion for this space.

Griffin Jones  27:15

I just had Kim Abernethy, from PCA interview me for my own show over Inside Reproductive Health. I don't know if that episode will come out before or after this interview airs. But as I was searching for the central theme of what that conversation ended up being I ended up titling the episode Should Fertility Companies Stop Taking Outside Funding, and then making a categorical assertion that they should stop taking outside money. It was more a call to attention to, for many companies, to invest more in the product market fit phase. That it takes a long time to do that, I see a lot of people burning out money before that's established. And then and then it's really hard. And I think more people could do some bootstrapping, and we might see it as the economy changes over the coming years. I do not say that that's a categorical prescription for everyone. And I know that there's a lot of limit to doing that in tech, especially with AI. Do you think it's even feasible to bootstrap in AI? Now that you're in now that, you've seen the money that you spent, the people that you've hired, the things that you've built? Is it possible to build it to bootstrap and an AI company in the biomedical space? And if it's not, is it possible up to even a certain phase?

Paxton Maeder-York  28:36

You know, it's a great question. I think, to a certain extent, I would hate to say something is impossible, right. And I would love to see someone go out and do it in a purely bootstrapped fashion, I think there are a few things that come to mind that make it very difficult. First off, artificial intelligence really requires an unbiased and very large and heterogeneous data set, that takes a lot of time to develop. And you typically need some sort of relationship or partnership to be able to, to gather that data, and a lot of folks rightly so right, this is really valuable data, you know, want to partner with a reputable company that has all the right data privacy and experts and PhDs that are, you know, it's an investment in both directions. So I think that's one component of it that would make it challenging. I also think that anytime you're doing things in medicine or medical device, there's a high regulatory burden. There are clinical trials and clinical studies that you have to publish. There's quality management systems and making sure that you're you know, following all the all the metrics so that it is medical grade software, and that requires a lot of investment. So you know, I think to do it right, I think it does require a really expert team and it takes a certain amount of time to get a product to the to MVP where you could go out and actually charge either you know, a clinic or you know, a patient or whoever might be your customer across healthcare. That isn't to say it couldn't be done. I think that there are other approaches that one could take to building artificial intelligence, especially if you already had access to a significant amount of data through different types of partnerships or relationships. But, you know, I think, while software is still a lot less capital intensive than robotics was and hardware, obviously, you have to build manufacturing, and, and all the rest, you know, I think it still does require a lot of capital to get these types of technologies off the ground. And more importantly, to do them, right. You know, and I think that's, that's where a lot gets lost data sciences, you know, it's, it's not that hard to get to an initial assessment or to, you know, the 85% mark, but that last 10 to 15% of performance is all the difference in the world between, you know, you know, making something that is reliable and safe for patients that's unbiased, and making something that's really more of a, you know, a school project. And I think, I think that's a huge delta that we're going to continue to see. And I don't just mean, within IVF, or even healthcare broadly, I think that's a problem that we're gonna see across AI, as this whole sector continues to grow. We see it in enterprise, we see it self driving cars, we see it everywhere. And so, you know, I think when you're talking about getting to that, you know, 99%, or point nine, nine, following, you know, it requires a really talented team and investment and thoughtful, you know, methodical development, and that that does require capital upfront.

Griffin Jones  31:31

So there are certain verticals where the barrier to entry is simply too expensive. There's high regulatory burden, there's a number of things that partners might need if they're going to help get a burgeoning company to the MVP phase, then how do you make sure that you don't burn through all of your dough while you're assessing product market fit? Because I see lots of companies that say, Man, you don't have it, like you just raised X million dollars, and you don't have anything that people are going to buy right here. You had, like, you saw the problem, the problem was there, I don't think any more studies would have more clearly revealed the problem or even talking to more customer necessarily would have revealed the problem, they got that part. They, they had some type of solution to bring to the marketplace. And it just didn't fit together, like a lot of these these companies that that don't make it or or maybe make it a little bit never returned the type of profit that they would be projected to do so for what they were valued at. How do you keep yourself from spending through too much money while you're assessing product market fit? 

Paxton Maeder-York  32:56

Well, it's a it's a philosophical debate, honestly, you know. I think there are tons of books out there that have discussed this exact problem, you know, Crossing the Chasm, and, you know, the proverbial valley of death. Of course, I think, you know, it's a few things, I think, one, there is a certain amount of discipline that's required, right. And, you know, we have a very strong, talented, but lean team, that is very intentional, you know, we were always trying to make sure that our burn as a company is on track with the development and making sure that we're validating what we've built, both from a clinical and science perspective, but also from a product market fit perspective. I'd also say that, you know, getting to MVP, this, the proverbial product market fit is is challenging and, you know, you kind of going back to my analogy earlier of pulling on the string, you know, you you may have one hypothesis about what a product might look like, that's going to bring significant amount of value, you may test that out, you may realize that's not where there's an enormous amount of value, and that there's additional capability you need to bake in so that it's a compelling sale on a compelling use case for the end customer. And that is to some level and art, I would say come over time. But I think in general, you know, I think folks that have worked in different types of industries and try to come to healthcare, I think, typically will struggle with this. It is healthcare, in general is a much slower moving market than traditional consumer or enterprise SAS. I think, you know, it requires wherewithal and long term thinking and a methodical march towards product introduction, and, and ultimately, you know, you know, getting the system out there so that it can benefit both clinicians and patients alike. And, you know, I think we saw that and in a variety of different stories. It's something that I certainly experienced firsthand when I was working on robotic surgery and that was an incredible success story at Oris. But it's just kind of the nature of the beast. And so, you know, I think making sure that what you're trying to build and In that you're constantly innovating, expanding the vision, making sure that you're adding functionality that is continuing to add and drive more value creation for your end users is just a constant process that we expect to be doing in perpetuity, along with all the incredible research that we're doing with our advisors and our clinical partners and other folks. And so as long as you, I think, plan ahead and know that that's what the road is going to look like, I think there's a path to being a success story. In medical technology, I think, you know, frankly, there was a tremendous amount of capital being deployed over the last five years or so. And there are a bunch of incredible ideas that got funded, that are really more point solutions, and may not ultimately be able to support the types of valuations or the long term value that, you know, venture community is expecting out of those companies. And so I think you're gonna see a couple fold, you're gonna see a couple companies, hopefully, life is one of them, that continues to do things best in class the right way, thinking strategically long term, and working towards towards those goals with the expertise in house, and then you're gonna see some level of consolidation, because we don't need a million different point solutions for all these different subcomponents, they should really all be, you know, part of the same ecosystem of solutions that can help, you know, improve the whole the whole sector. So those are some of the things that come to mind when thinking about, you know, how do you how do you not burn out? And how do you match your capital raising with your burn with the stage of business that you're at, especially within healthcare.

Griffin Jones  36:35

You talked about needing to be prepared for that long haul, does that mean you need to match with VCs who are also prepared for that long haul? And is that something that's realistic to expect from VC? So you talked about the art of managing the product market fit. And when you bootstrap, it's it's pretty obvious. So you run out of money, then you figure out a way until it starts making money. When you when you're playing with other people's money it's different. And you mentioned that because healthcare has such a high regulatory barrier to entry move so much slower than other sectors might be used to, should we expect to see VC firms and not just like, you know, arms of VC firms, but should we expect to see VC firms that are exclusively dedicated to healthcare? Is that an upward trend? Is that not happening as much? Is, is that necessary? Because if it does take this long, then you need the funding to match how long it's going to take. And some people might not be ready for that? 

Paxton Maeder-York  37:37

Yeah, you know, I think, first I'd say that there are a variety of different types of investors. And I think that's really important for anyone going out and trying to fundraise, right? There is, you know, there are folks that only do enterprise deals. There are folks that don't touch healthcare. There are a lot of investors that don't particularly want to invest in women's health, for example, or human health, you know. And I think whenever going out to fundraise, you really have to be thoughtful. And again, going back to this dating theme of figuring out who the right folks are to be talking to and, and who has both interest wherewithal and long term vision that can share, you know, kind of where you want to take the business as a leadership team. To answer your other question. Absolutely. There are plenty of healthcare focused founders, or investors and founders. And I also think that the personally, I've found that the style of investment between East Coast and even West Coast varies, and one of the things I'm really grateful of is that I've got both East Coast and West Coast firms on my cap table, and I kind of have been able to accumulate a hybrid of those two different, you know, approaches to investing. And, you know, I think, again, it's it's really just about finding people that believe in the long term vision, see the high level opportunity that exists here, who have been through the pain point, for example, on their own, so that they know, okay, like this is a problem this, this whole sector is going to continue to need to grow, there's going to need to be better technology and analytics can an AI can play an important role on that. And and we see that opportunity down down the line. And you know, as long as the team is thoughtful about how they're spending that cash in very value creative and additive activities, then hopefully, in the long run, you're gonna go out and achieve that goal. So yeah, I mean, people talk a lot about patient capital. I think there, there are certainly funds that, you know, don't expect to return in the same, you know, eight year timeline as others. There's kind of evergreen funds, there's traditional private equity, which has a more much shorter time period of trying to get a return on their capital. So all those things need to be taken into account. But what one of the things that's been so wonderful that I found along my journey is that those investors do exist. There are definitively folks out there who come from incredible firms that believe in the long term envision and are willing to put capital behind things that matter both for the social good, and behind teams that they think are qualified to go out and make that type of difference.

Griffin Jones  40:09

Are you raising money right now? Are you moving on to a Series B?

Paxton Maeder-York  40:12

We're not raising at the moment, we're still heads down and developing a ton and, you know, working with our close partners to get our products out into the field, but we will continue to raise over the course of the lifecycle of the company. And, you know, I think there are a variety of different applications and use cases for that capital beyond just keeping the lights on and continuing to pay salaries and make sure that we're, you know, ever developing more and more of our core platform. You know, there's, there's lots of applications that you can use capital at the right times to supercharge and enhance what you're building. And given our goal is to supercharge and enhance, you know, the clinical care in in practice, the same thing goes for the right investors who have the right almost investor products that can work with great companies like ours.

Griffin Jones  40:57

So your last round your series, they finished when?

Paxton Maeder-York  41:00

A year ago in March.

Griffin Jones  41:02

How much has the market changed in terms of venture capital in the last year and a half since since March of 22? From what you can tell from your, your investors now, your peers, what's happening in Silicon Valley?

Paxton Maeder-York  41:20

So, you know, you can you can read the investor reports, you know, I think we're all looking at the same numbers, there certainly has been a decline in, you know, in both digital health IPOs traditional tech IPOs share prices are down at times, although they they fluctuate, obviously, and certainly, you know, smaller rounds, and where you're expected to be by the time you raise that round has, has evolved. Having said that, you know, I think there's an old adage that the best companies are built during downtime. And I think that's true, I think there was certainly a period where there was so much capital that was being deployed so quickly, people weren't getting to know their investors, and the investors not necessarily getting some of the portfolio companies that, you know, there was a lot of stuff that maybe shouldn't have been funded during that period. And I think those types of businesses that don't have kind of a strong long term goal, and you know, industry or market tailwinds behind them, I think some of those may struggle in the next year or so as they start to ramp up.

Griffin Jones  42:18

Are they still getting funded? Are you still seeing jokers get funded?

Paxton Maeder-York  42:22

I would hesitate to call anybody a joker. But you know, I think to a lesser degree, although, you know, I think Artificial intelligence has certainly become more of a hype term recently. We've been doing this for three years. I think the underlying data science that is backing this type of technology is super solid and real. Having said that, you know, I think unfortunately, there will be folks that may not have spent the time to really become experts in data science, are going to start companies and I don't just mean this in healthcare, I mean, this across the entire tech ecosystem. And you know, hopefully those companies don't, you know, do things that may harm the overarching ecosystem of technology implementation, which is really what we're talking about here, right, you know, AI is, you know, is a is an ever evolving field of data science. And it's based on having these large datasets and how you apply those datasets to real world problems, is, you know, where rubber meets the road, and you're building real businesses. So, you know, I think, I think there will always be some level of FOMO and venture hype that funds different types of companies. But, you know, I think for the folks that are in healthcare, specifically, infertility and IVF, is not going anywhere. If anything, we know that we're not meeting the level of supply that we need to meet the demand. So you know, I think it's a it's a fairly, you know, robust bet to make. Alright, there's, there's a real need here for the population, it's a growing market, you know, there's opportunity to bring technology and best practices, not only from across the United States, but also internationally and globally. And software and AI has this like, really remarkable, unique capability to make that a reality, and a in a very usable and impactful way. So I think from a high level perspective, you know, the, the trajectory in the vision makes perfect sense. I think, of course, then it comes down to well, are you going to be a best in class company? Are you going to do it with high integrity and really do all the clinical validation and make sure that what you're building is, is robust? And that all comes down to you know, how experienced is your team and whether or not you guys have the right mindset to go out and march towards that long term goal.

Griffin Jones  44:38

You haven't acquired any companies in this three year tenure have you?

Paxton Maeder-York  44:42

Not yet. M&A is certainly something that we are considering and when will probably will be part of our story in the long run. But right now, we really view what we're building today the Alife Assist platform, which, you know, is built for reproductive endocrinology to optimize ovarian stimulation embryology team seem to automate and digitalize their platforms. And then, you know, clinic management, that system, we believe is the core of a lot of opportunity to continue to bring this type of value to the clinic.

Griffin Jones  45:11

Did you consider any M&A and building that system?

Paxton Maeder-York  45:14

You know, we have along the way, we've looked at a number of different opportunities, and nothing is really, you know, positioned itself to us in a way that made us feel like this is something that is going to be accelerating our trajectory into the market. You know, there have been other companies that we've partnered with some companies have already come and gone. There, there are companies that you know, and team members, in fact, that used to work at other companies that we've kind of encouraged them to, hey, join our story, because we think we've got a great, you know, great team, great backers, and the right vision and the right resources to go out and get it. But you know, to date, it hasn't made sense to acquire any smaller companies yet.

Griffin Jones  45:54

I'm seeing if I can glean from you any kind of criteria of when it's too early. It seems to me that some companies are acquiring companies too early, but I'm just, that's just me, being a Monday morning quarterback, I don't know. And so I'm trying to see if if there is like any kind of criteria set where it's like, now this, you have to wait until X until it really makes sense to start paying for other companies.

Paxton Maeder-York  46:21

Yeah, I mean, you know, I think there's a difference between, you know, acquiring another business and merging with another business. And, of course, you know, the stage of business, you know, company that you're at, will dictate, you know, there, there are, you know, two plus two makes five situations where, you know, one company is kind of struggling and other companies doing well but kind of struggling together they have a much better shot. I think for for Alife specifically, and I can only really speak from our position, I think there are a number of different opportunities that we're constantly seeing out in the market, and that we know long term we would like to either partner with, acquire or build ourselves. But the way I think about it is I really want to hang those different types of new opportunities off of a core foundation that we've built. And right now being Series A, and having recently launched our products and are now you know, you know, working very closely with partners to continue to push them out into the market and get real world utilization, they're constantly getting better as we get more feedback. You know, that's, that's kind of stage where we are, as the as this platform, you know, hopefully resonates with our end customers and becomes adopted. And it's something that is really impacting clinical care for doctors and patients alike. You know, that's where we can start having really interesting conversations about like, what would be additive to our platform, what are some other things that we're in a unique position with either our data or the infrastructure we built, that is going to make us even more competitive if we either acquire or build some of these additional business opportunities on our own. So, you know, I think post Series B, Series C, that's typically where you see a lot of tech companies starting to do real M&A, with the exception of kind of early stage seed combinations that, you know, for folks that are just trying to continue to survive as businesses,

Griffin Jones  48:06

Let's wrap with the team and the tech stack, I don't expect you to go into anything proprietary about your tech stack, but to the level of detail that you can share, what does it look like just for someone that it might be a fertility doctor has never worked for an AI company? What does the whole tech side, which is the majority of what you're delivering, look like? Because there's a product teams, the the CTO, the tech stack, to the level that you can share? 

Paxton Maeder-York  48:35

Yeah, I think, you know, without getting too too deep into the technical side, because, you know, I think people are probably less interested in, you know, what, what back end resources were using as a company, I think that one of the things that can, that can be very, very useful is thinking about building a company almost the same way as you think about building a product. Applying engineering mindset to your organization. And so, you know, for us, we when we started the business, you know, we really were focused on R&D, and developing the early platform. And so you know, what that looked like from a leadership perspective is we had a had a software, I had a product and I had a data science, and each one ran their own divisions and data science was building new algorithms was publishing papers, was speaking at conferences, the software division was actually building the core infrastructure, taking the code from data science and haven't you know, making sure that it was going to run reliably, you know, making sure that we're doing all the documentation and testing, verification, validation testing is super important and medical technology. And then product was really focused in both the design of the front end user interface as well as you know, talking to all our partners and testing and making sure that what we were building was fitting that Venn diagram we talked about earlier. As the company has evolved, you know, we're constantly changing our organizational structure to meet the needs of the business at that base. So as we started to launch product, we brought on a head of Clinical Affairs to run a lot of our clinical studies and RCTs. We started to build relationships in Europe, so we have a head of head of EMEA based in Zurich. We actually have a wholly owned subsidiary based in Zurich to build partnerships across the EU really focused on trying to bring this vision of best practices from around the world to that patient that walks in the door at a specific clinic. And then we consolidated some of the units as well as brought on now head of commercial that's going to help us continue to drive the products and their adoption. So it's kind of a constant, you know, re-evaluation of where we are with the phase of the business. Are we in R&D? Are we commercial, you know, switching over to early commercial phase? But you know, I think really making sure that your team is structured in a way that allows you to go out and thoughtfully and efficiently go out and build what you want to build is, is I think paramount when you're starting your own company.

Griffin Jones  50:55

Paxton Maeder-York, thank you very much for coming on the Inside Reproductive Health podcast.

Paxton Maeder-York  51:00

Thank you so much for having me. It was a pleasure to be here.

Sponsor  51:03

This episode was brought to you by Embie. To discover where your time is going and how Embie can transform your clinics efficiency, visit us at embieclinic.com/report. That's embieclinic.com/report. You've been listening to the Inside Reproductive Health podcast with Griffin Jones. If you are ready to take action to make sure that your practice thrives beyond the revolutionary changes that are happening in our field and in society, visit fertilitybridge.com to begin the first piece of the fertility marketing system, The Goal and Competitive Diagnostic. Thank you for listening to Inside Reproductive Health.

182 6 Barriers To Automating The IVF Lab, Featuring Eva Schenkman and Helena Russell



What is stopping IVF labs from becoming fully automated? Tune in to this week’s episode of Inside Reproductive Health, as Griffin Jones sits down with Eva Schenkman and Helena Russell of ARTLAB to breakdown the six main barriers to automating the IVF lab.

Listen to Hear About:

  • Why automation isn’t happening in certain areas of the IVF lab.

  • Risk and inefficiency of data entry.

  • Lack of trust that comes from business intelligence software.

  • Lack of adoption of the Vienna consensus.

  • Which metrics are meaningful for safety that don’t necessarily improve clinical outcomes, but are required to improve safety and productivity.

  • Delivery vs operations- what needs to be prioritized now vs. what should be prioritized for the future.

Website: www.artlabconsulting.com

Eva’s LinkedIn: https://www.linkedin.com/in/eva-schenkman-ms-phd-cc-eld-hcld-6121778/

Helena’s LinkedIn: https://www.linkedin.com/in/helena-russell-5aa60214/

Transcript


Eva Schenkman  00:00

They're missing the point that you know I think UCSF did some data where they showed that having an embryo scope in their lab saves them the equivalent of one embryologist time per day. And if you look at the cost of an embryo scope which is probably akin to about you know, one year embryologist salary that is becoming more efficient with these devices will in the long run, save you money, especially now when there is no embryologist to be found.


Griffin Jones  00:32

All of the change that is not happening in the IVF lab we talk all about the automation is coming to the field and seemingly every talk at every conference many episodes on, I want to know why hasn't it happened already? Why isn't it happening faster. And so I explore those obstacles and barriers with my two guests on today's program. That's Dr. Eva Schenkman. She was a lab manager for a number of years to different practices. She has been a consultant. She now runs a program called ART Lab. And I bring in her colleague Helena Russell, and we talk about the barriers to implementing automation categorically. In the IVF lab, we talked about the risk and inefficiency of data entry, we talked about the lack of trust in the data that comes from business intelligence software, if estimates that fewer than 10% of IVF labs have fully automated their data entry with business intelligence software, we talk about the Vienna consensus. Why has there been a lack of adoption in the Vienna consensus again, I asked Helena and Eva just a ballpark how many labs they think have adopted the Vienna consensus. And I'm asking them to do this off the top of their head, but they think it's about half that have adopted some meaningful level of the Vienna consensus. We talk about other metrics that are meaningful for efficiency and safety that don't necessarily improve clinical outcome, but are necessary for improving safety efficiency. And for activity. We talked about this person dynamic between delivery and operations where you are on the hook for doing a certain number of IVF cycles, you're on the hook for serving a certain number of patients, you have to do that to make payroll to keep the lights on to keep the patients happy. Meanwhile, there's the operational systems behind that which are another entity another chore to solve. And those two things are at odds of each other in terms of what is prioritized now in the moment, but what needs to be prioritized and improved for the future and for ongoing delivery. Finally, Helena and Eva say that some solutions are not ready for primetime and boy do they go to town on naming who those folks are? Now they don't try to get them to but of course they go hard and ideas and soft on people as is generally good advice. So it was a constellation for myself, I have to detail what they would like to see from RCTs what they think is missing from solutions that are coming to the via what they think needs to be proved in order for solutions to merit much wider adoption and what IVF centers could do in the meantime to help prove the concept. Enjoy today's episode with Helena Russell and Dr. Eva, Schenkman, Dr. Schenkman, Eva, Ms. Russell, Helena, welcome to Inside Reproductive Health.


Helena Russell 03:19

Thank you, it's great to be here.

Eva Schenkman 03:20

Thank you.


Griffin Jones  03:22

I've finally fulfilled the promise or I'm living up to a promise where I said it was going to create more IVF lab content than I have in the past. I think, this year, we've already done more episodes about the lab than we did in the first three years of the show, combined. So I'm starting to have a rudimentary level of knowledge to where I can maybe start to ask more interesting questions. And one of the things that I want to talk about today is the obstacles behind the automation for the lab. So at a high level, on the show before I've talked about the automation that's coming to the lab, and like to take advantage, speaking with each of you about why it isn't happening faster, and probably have you unpack and give specific examples as we go. But maybe we start at a high level, with just the automation that you're seeing in the lab happening right now that you weren't seeing five years ago, and maybe not even two years ago, what's happening with regard automation.


Eva Schenkman  04:25

Now, one of the ways in which, you know, I've been involved in some of my consulting activities in some of the automation is through data analysis. You know, we spend an awful lot of time in the lab, you know, crunching numbers. And in most labs, we still do it the same way we did 30 years ago, which is, you know, we've usually got two or three different Excel spreadsheets, we've got one for data, we've got one for cryo, you know, we may also be entering something 20 or more, and we used to sit there at the end of the month or the end of a quarter and spend, you know, 234 days to crunch all those numbers. So not only counting the amount of time that embryol Just spending putting in all that data, you know, risking all those data transcription errors, you know, now we've been using things, you know, business intelligence software, like Power BI, to pull that data automatically out of the IVF EMRs, to run that data in real time, so kind of call that real time analytics. So that I see is one of the key ways into which we can save, you know, an enormous amount of time making the labs, you know, a lot more efficient, is on a data analysis standpoint, you know, one of the big talks now with a lot of the meetings or on automation in the lab and efficiencies in the lab, and, and, you know, I think we can talk a little bit more more about that, what the roadblocks are, you know, to those. And, you know, to a long way, I think a lot of the roadblocks are One is cost, you know, a lot of these devices, things like, you know, an embryo scope, for example, are very expensive. And, you know, a lot of physicians or a lot of practices expect to see, oh, I'm gonna get this device, it's going to increase my pregnancy rates, oh, it doesn't increase my pregnancy rates, well, that I'm not investing that kind of, you know, money into it. But they're missing the point that, you know, I think UCSF did some data where they showed that having an embryo scope in their lab saves them the equivalent of one embryologist time per day. And if you look at the cost of an embryo scope, which is probably akin to about, you know, one year embryologist salary, that it becoming more efficient with these devices, will in the long run, save you money, especially now when there is no embryologist to be found. You know, and I think some of the other issues I see with the automation is things are rushed to market quickly, you know, at at a very high price, and they don't necessarily have you know, a lot of the data behind it yet, that you know, that it is going to be you know, just just to save for just the same as a senior embryologist. So I think kind of got, you know, a couple of issues there, you know, between the cost and, and the efficiency, and, you know, making sure that you know, that we can get get current staff to adopt, you know, this new technologies,


Griffin Jones  06:59

because you give me a couple of different avenues that I could further explore. Let's start with the spreadsheets. You mentioned, having two or three Excel spreadsheets previously, for which you need for your data analysis. What were they what what were their roles, those those spreadsheets and the information that they contain


Eva Schenkman  07:19

everything from, you know, you're doing your pregnancy rates, your competency assessments, also your CRO inventory, you know, we typically, for the most part, still keep paper worksheets in the lab, very few of us are using, you know, tablets or have gone paperless. So, you know, we've got that paper, you know, we're either scanning that paper into an EMR or, you know, retyping that data into an EMR. And then typically, a lot of the EMRs, don't do data analysis very well. A lot of them don't have reports that follow the Vienna consensus, you know, guidelines. So we're then keeping separate spreadsheets, so we're putting things into the EMR, putting things into, you know, Excel spreadsheet for data analysis, and then typically having a third sheet for, you know, cryo inventory. So we're entering everything, you know, typically three times, and then taking having somebody you know, typically higher up, then do all of that data analysis, like I said, usually typically the end of the month, sometimes at the end of the quarter,


Griffin Jones  08:17

how is QA done in this instance, when you have three different sources of information, but they're all in different places? How, how is QA done so that the duplicate of information is correct, because anytime you have information, different sources that isn't uniformly exported, you always risk you


Eva Schenkman  08:37

typically an Excel worksheet, you hope you catch it, there's not really a lot of a lot of formulas in there to kind of automate to to pick that up. You're always gonna get data, transcription errors, some of the things like Power BI can can pick that up for you. But I think, you know, honestly, a lot of times it gets caught when you're giving a patient data off of your cryo Inventory spreadsheet and a patient, you know, or nurse, correct shoe, you know, will will that's, that's wrong. That's not what we had, you know, so that that is a problem, you know, with data entry errors, is we really don't have a good mechanism to ensure that the data is accurate.


Griffin Jones  09:14

So when you have three sources of info like that, you got your spreadsheet for cryo inventory, you're scanning into the EMR, and then you've got a separate spreadsheet for the data analysis. There generally isn't like an overarching QA for the data entry to make sure they're all uniform. Now, okay, so even without regard to efficiency, there's still there's a risk there.


Eva Schenkman  09:36

Yeah, absolutely. You know, your data is only as good as the information you're putting in.


Griffin Jones  09:41

You mentioned that is an area where clinics are starting to automate more and those spreadsheets are being supplanted or that's something that you envisioned in


Eva Schenkman  09:51

the know there actually is is a few systems out there. Several of the EMRs have been using business intelligence software either through Tableau or through Power BI and linking those with their EMRs to that automatically pull that data out of the EMR. So as soon as you've done your first check, you know, as soon as you've done, you know, your, you know, your observation or the pregnancy data is entered in, it's pulling it into those Power BI sheets. And those not only that are automated, but they can even be set up to then watch you when there's a problem. So they can send you notifications that, you know, Hey, your XC three P and rate is starting to creep up. So you can, you know, definitely not only from an efficiency standpoint, but also from a troubleshooting standpoint. So I know, you know, recently one of the media companies had an issue with with some oil, for example, you know, and that, you know, typically tends to take a little bit of time until you're able to pinpoint what the problem is. And you know, the hope is that these automated systems would be able to pick up on something like that much quicker than you'd notice by eye or, you know, you got to wait till the end of the month, you know, obviously, something's killing all your embryos, you'll notice that pretty quickly, but let's just say you've got, you know, 25%, drop and blast conversion rates, that may not be something you pick up so easily, maybe you had some bad patients in there. But you can use a lot of that business intelligence software, it's been used by the, you know, financial industry and other industries for for years, you know, now we can kind of harvest the power of that, and and use for the IVF labs,


Griffin Jones  11:20

do you have even a ballpark guess, of what percentage of IVF labs are now automating their data entry with business intelligence software?


Helena Russell  11:30

Automating? I'd say, single digits?


Griffin Jones  11:33

That's a very, very low, yep. What's stopping it from being at 90 100%?


Eva Schenkman  11:39

I think one is trusting in the data. Two is, is, you know, we, for as much as we like to think we're ever changing, we don't actually like to change that much. You know, we don't want to let go of our paper worksheets, we, you know, this is, this is what we've done for 30 years, you know, we don't want to make mistakes, and what we do we know that, you know, an Excel spreadsheet, you know, as long as it's not, you know, sorted wrong or tampered with, you know, it will get you the, you know, the data that that you need, you know, a lot of the EMRs aren't necessarily don't necessarily have the best fertility modules. So, you know, even, you know, a lot of people in the lab, they're, they're still using the paper worksheets, and they're only scanning in their sheets. So one is, is, you know, if you're going to use something like Power BI or Tableau, you really have to have a dynamic EMR, to be able to use that with so. So that's something a lot of the clinics struggle with, you know, and I think just just trusting, trusting in the data is a bit of a learning curve, you know, to to get going with it. And, you know, I think slowly it's, it's starting to come come about, but, you know, slowly,


Griffin Jones  12:46

by the way, Helena, anytime that you want to jump in, I tend to just riff off questions, because I


Helena Russell  12:51

just want to say a couple of things to, to kind of, you know, kind of chime in with Eva, one thing, that's what's really challenging is learning curve, because it's not just trust, it's taking somebody who works with their hands, and putting them into a situation where they're going to have to be working with computers more. And that can be a little daunting. But again, having the right tool and the right support from that tool, helps us something else that even just said, is that they're not, not all of these EMRs are created the same. And that's true across healthcare industry, in general, you know, they're very unique, there are so many out there. And they do different things differently. And so there may be some that are a little bit better for gathering all the information that needs to be gathered, and also to be flexible enough. One thing that you may or may not realize about IVF is that not all IVF centers do things exactly the same way. So you have to be flexible. And the learning curve is one of the one of the things that I think is challenging for people and trust, like Eva said, another way of automating that kind of tails into EMRs. And specifically EMRs built for IVF is witnessing, which is an automated system these days with barcode reading or with radio frequency. And even might want to chime in on this one as well. She has a lot of familiarity with these. And those are also tying in with some of these IVF databases, or electronic medical record systems. And again, pulling a lot of really good valuable information from the lab into that system helps with once we get to that point where we can do the analysis via you know, Power BI, what we can then do is really target quality control, quality enhancement, and quality assurance.


Griffin Jones  14:56

Let's stay on that thread for a second before we get into workflow variance and And the barrier of change. You mentioned one of the issues apart from that is trusting the data itself. So what is the cause for mistrust and data? Or what is the risk of inaccurate or incorrect data in using business intelligence software for data entry,


Eva Schenkman  15:18

when you're pulling data from from an EMR, you know, one of the problems is, these EMRs are all structured differently, you know, they're usually large back end SQL databases, they may not be, so you can't take, you know, three different EMRs take the same Power BI software setup and plug it into these three different systems, they won't work, you know, so these things have to be customized, you know, unless it's something your EMR is already offering, they, they would then have to be customized to each setup. And a lot of it is just in that analysis, knowing you might have two or 3000 different fields on the back end, to pull from, you know, how are you? How is each lab recording that data? Where are they? Where is that data sitting in the SQL? databases for analysis? I think some of it might be generational, you know, I think, you know, the first first generation of embryologist, you know, even though we're we're, you know, we are pretty good at using computers, you know, we, for the most part for the last 30 years have done everything on paper, have done everything, you know, simply the second we have to trust, setting up those scripts and setting up something to to the IT department, you know, it's these things are very difficult to validate. So it's a lot of time, and one of the things we don't have right now is a lot of time in the lab. So I think part of that is, is having the time to validate these systems to trust them, it would be very hard for company to come in to develop, you know, a Power BI software, that's, that's applicable to all EMRs. Because the EMRs are all structured differently. So they need to be done, you know, on a customized or bespoke, you know, level between between each system. But I think it's just as I said, I think it'll be different with this new generation of embryologist coming through, I think they expect it, you know, they practically live with a phone, you know, in their hand, you know, I think they're going to be a bit more comfortable with with having this data. Automated?


Griffin Jones 17:11

Tell me a little bit more about what you mean, by the time it takes to validate systems? Does it mean to like pilot the program to check the…


Eva Schenkman  17:20

Yeah, you know, I'm actually involved with one, you know, right now looking at at some of these, these automated reports, and I have to go into the EMR and I put in test cycles, and I'm putting in, you know, different complicated ones with day one xe or with late for some with thaw biopsy, refreezes, combination cycles with fresh and frozen eggs. And all of these data sets are stored in different tables in the back end of the CMR. So that I have to sit with the IT people and structure each of these queries. And, you know, we tested on these cycles, and, you know, these, how do you tell an IT person, you know, when they're doing a competency for, you know, good day three cleavage rate? You know, for example, you know, what does the word good mean? You know, if you asked, you know, for embryologist, you're gonna get five different answers, you know, and that's part of why, you know, we rely on things like the Vienna consensus, you know, as a standard, you know, guideline to go through, but then, you know, each and every clinic, we roll these things out to, has to validate it on their own, because none of us are doing recording data the same way, you know, there's, you know, we all record it a little bit differently, we're all using different templates, we're all using, you know, different embryo grading criteria. So I think that's part of, you know, a bit of a problem with it, you know, I think but, you know, as clinic start to see the benefit of these systems, I think it'd be easier and easier, you know, we get these things validated, we get a couple of hopefully, key key labs, you know, incorporating them into their workflow. You know, I think we'll, you know, we'll kind of get the message out there, that the systems are, you know, are reliable or trustworthy. And, you know, that'll go a long way to really making the labs, you know, more efficient. Everybody's talking about, you know, lab on a chip and everything else. But, you know, I think, you know, when you're embryologist are spending a significant amount of their time being admins, you know, hand entering data is still using paper worksheets. Were a long way away from talking about, you know, lab on a chip.


Griffin Jones  19:18

How much chicken and egg is happening here, like, if part of the reason why labs are slow to adopt the technology, they're slow to validate the systems because there's so much variance in workflow, people report data differently, they grade embryos differently, how much of so that's the barrier, but it's also the result, isn't it? Like if you had the universal systems implemented, that you might have a more universal way of recording data, you might have a more universal Is that happening?


Eva Schenkman  19:51

We have the Vienna consensus, you know, the paper that was written for KPIs. I think that goes you know, along A great deal.


Griffin Jones  20:01

Okay, what is stopping people from categorically adopting this Vienna consensus across all labs?


Eva Schenkman  20:10

I think for the most part, it's been very well, you know, received, I think it's just it's that the woods that way, we've been doing it for 30 years. You know, it's, it's that belief, it's, it's worked for all this time, you know, this is, you know, in that belief that, that, you know, we're kind of all homegrown cooks in each of our labs, that, you know, we kind of, we kind of do it our way, these are the KPIs that, that that worked for us, there are still some labs that are doing d3 biopsy, you know, as opposed to, you know, blastocyst biopsy and slow freezing, it's just that ingrained, you know, because we don't want to make mistakes and in what we do, so in some ways, we're very reluctant to try new things. And, and part of that comes with doing it the same way it's worked, we don't want to change it, but and


Helena Russell  20:54

so much hinges on it, right? Yeah.


Eva Schenkman  20:59

And that first generation of embryologist is retiring. They're leaving the field. So, you know, I think it's, it's, it's important to, you know, this new generation, they're not going to sit there for the, you know, the amount of hours and hours and hours that we spent typing into three, you know, three databases, they want to enter things on a tablet, you know, they don't want to enter things on on paper and then transcribe so, you know, I think there is a lot of push from, from these newer embryologist to to automate things, you know, and, and hopefully, you know, we'll get some significant changes. They're


Helena Russell  21:31

more comfortable trusting the data, as Eva has said,


Griffin Jones  21:35

what percentage of labs is, if you can even ballpark it? Do you suppose have adopted the Vienna consensus to? If not to the letter, you know, 90%?


Eva Schenkman  21:46

I'd probably have to say, maybe, what do you think Elena, close to 50? Probably


Helena Russell  21:53

I still they're not accepting all of them. They're probably focusing in on a few Don't you think? Eva?


Eva Schenkman  21:58

I think so. I'm still surprised how many lab people I speak to who haven't heard of it. And, you know, as I said, each one typically has their own KPIs.


Griffin Jones  22:06

Thank you, Eva. Now, I don't feel as dumb for asking.


Helena Russell  22:08

Yep. It's unfortunate. And I think it's a lack of communication in our field. But I also think that what we're doing is very difficult. And so the challenge is making sure that we continue to be able to produce what it is our patients need. And to meet our patients needs. I mean, there, there's, there's no excuse for failure. And so when you have something working, it's difficult to hear what somebody else is saying, if it doesn't mean an improvement, which I think you've kind of hit on earlier, unless you can show a, you know, a positive outcome. And it may be that they'd rather spend that extra money to have somebody do something in a less efficient way, then trust in something that may not may or may not give them the outcomes that they are looking for. Yeah, is


Eva Schenkman  23:06

it’s difficult to trust in the scripts that are written by, you know, by someone with a computer background that, you know, you as an embryologist don't really understand. So as I said, that's why the validation of it is so important, get them seeing that this data is accurate, and is pulling correctly. And, you know, I think, you know, to be able to have an automated system like that, then alert you, not only when something is out of range, but as deviating towards being out of range, I think will be you know, will be invaluable. And, you know, this, you know, one issue that recently developed with oil is now resulting in a class potentially, you know, class action lawsuit. So, I think, you know, anytime we can develop something that would pick up on these things, not only tell us our what our pregnancy rate is and what our our individual embryologist competency rates are, but to be able to then alert us to any troubleshooting issues in the lab, that we don't have to wait six weeks, you know, now we see something in our data analysis. Now we have to try to figure out, you know, figure out what it is, you know, that's where we're using AI is also going to help at some point, you know, with analyzing this data.


Griffin Jones  24:11

So I'm understanding if there's not a clear clinical outcome that lab directors can see of in terms of success rates, that there often isn't the impetus to impose a change, and I see the agents working against change. We've done it this way forever. It's worked this way forever. We have a big variance in workflow from one place to another. So just because it worked for these guys over here doesn't mean that I know that it's going to work over here, but at this point, why isn't the shortage of embryol embryologist and the constraint on embryologist time enough to have made a bigger catalyst for change? seems like to me it seems like okay, if success rates are equal, but I can get back an embryologist day. Every time that we use this solution, or I can get back this many hours of embryologist time, why is that not enough of a catalyst to be seen way more automation than we're currently seeing?


Helena Russell  25:22

Part of it has to do with time, it takes time to train somebody to do something new. You know, if you're so overwhelmed in your lab or your IVF facility, and you don't have enough time to train a new person, you don't have time to learn something new, don't you think? Eva?


Eva Schenkman  25:44

I think so. And I think it's just that you know, exactly that you don't have time to train something new, it's that chicken and egg, you know, scenario, again, you know, I'm so overwhelmed, I not only have time to not train somebody, and then you say, Oh, well, you know, get this piece of equipment or whatever, for automation, there is going to be a period of time where that, you know, system is going to actually take you more time, until you you know, you wreck it, you know, you're able to be proficient at it and you're able to, to realize its efficiency. And, you know, not all people have the patience for that much time for adopting it and the cost, you know, all of these, these automated systems are very expensive. So getting physicians in groups and practices, it's easy to say, I need another embryologist and they'll pay, you know, six figures. Plus, for an embryologist who see a body sitting there, you know, to pay six figures plus for a piece of equipment sitting on the counter, you don't see the efficiency savings as easily as you see another body sitting there. So I think that's part of it. And without them seeing, you know, like, as I said it, you know, I go back to time lapse, you know, they there was just, you know, paper recently that, you know, basically is, you know, we shouldn't be, you know, looking at time lapse, because there's we didn't see an improvement in pregnancy rate, but you're missing, you know, the picture of it, you're missing, you know, the safety of it, you're not having to take the embryos out to look at them, you can monitor embryos remotely, you know, so if there is, you know, more COVID outbreaks or another pandemic, you know, you can check fertilization from from home. And, you know, just that


Griffin Jones  27:18

you could centralize embryologist could knew or at least part of that workflow,


Eva Schenkman  27:23

you could do you have offsite lab directors could monitor things remotely, they can log in and look at the embryos look at how they're growing, you know, pull the data, you can see these Power BI apps, you can see all of your data on your mobile device, you can even see the images of your embryos on your mobile device. So I think it's, it's, it's, it's that cost barrier, but it is that learning barrier, that it's just not something new that we've done. And, you know, I think you'll I think next years, there'll be some workshops, at some of the meetings that are going to be focusing on future of technology and innovation, and where where things are going to be, but not just theoretical, but actual practical, what's here, what's now you know, what can we kick the tires on now, and part of that is, is training and having these new innovative systems launched at the at training centers, and having a rail just come in and use them because nobody wants to practice on a real patient. You know, you need to be able to have a place that's comfortable, that you can go in and you know, learn this in an environment that's not stressful, you know, not while you're you're trying to, you know, to do real patient samples, that you have a place to get comfortable with these devices and, and to you know, learn how they work.


Helena Russell  28:36

And we're all monitoring is integrated. And I mean, yeah, looking at your incubator, your temperature, your co2 level, your oxygen level, looking to see if your liquid nitrogen tank is got enough liquid nitrogen tank, liquid nitrogen in it, making sure your refrigerators are performing up to par. And having those be part of your automated, automated integrated system so that you literally have every function that you would normally assigned to possibly, you know, an intern or a novice embryologist, somebody who's a junior who's just coming in. Instead, you can have continuous monitoring, which I think is extraordinarily reassuring. Probably there's a role for someone or company out there to help clinics bundle and to become efficiency experts. I think one of the things that our training center does is helped expose new embryologist and even in workshops where we're opening up our center to experienced embryologist to come in to have one or two day workshops, they will be exposed to those kinds of integrated systems as well. And you know, a lot of it has to do with you know, I can I can hear about it all day long. I can read about it all day long. But if I can touch it, and I can move the dials and nobody's sample is going to get hurt by that. And I can actually download an app and do it on my own phone or my, you know, my iPad, while I'm in this Training Center. You know, the


Griffin Jones  30:13

exposure that you're talking about in the training center accounts for some of the issues, the distrust in the data, the lack of familiarity, the validation of the system counts, for some of them. Some of the things that it doesn't like, what you've been talking about is something that I've been obsessing over with regard to my own business and business in general. And I think we can apply it to the IVF lab, and that is delivery versus operations. And often when you hear business books, or you hear business talks, operations, and delivery are almost used interchangeably, like delivery, meaning the fulfillment of the good or service, which we've sold or promise and operations is really the system behind it. So we're roofers, our delivery is we're going to have a new tear off roof on your house by the end of April. That's the delivery. And we have an obligation once that roof is sold to fulfill that deliver, you could use delivery and fulfillment interchangeably. But operations is the system behind that delivery. So delivery is getting the roof on the darn house getting it done by the date, we said we were going to get it done by but operations is what types of materials we buy the workflow behind it, who we assigned to the job, how the job is assigned and accounted for and reported on the QA that comes after it the what what we automate or don't automate. And, and all of that is operations. And there's a tension between delivery and operations, because you have delivery obligations that you have patients cycling through, and you have a finite number of embryologist that can work on those embryos, while those patients are being served while you need to make this institutional change at the operational level. So how do you solve for that how, in this specific to the IVF lab, how do you begin to relieve some delivery obligations, while investing in the operations that will ultimately result in a virtuous cycle.


Eva Schenkman  32:35

Part of what we have here as opposed to just also having, you know, kind of a training facility is is you know, our training facilities a fully functioning mock IVF lab. So one to have all of these different systems communicating here. So that when people do come and try them, it's not just trying one piece of it, it's kind of seeing, you know, the entire system working as if this, this was a functioning lab, the other thing we have to convince them of is, is you know what to do when it goes down, because that's one of the most common things, you know, I hear that if we're going to be entering things on a tablet, or we're going to be entering things, you know, when our mobile device, you know, data patient data is potentially going up into the cloud, you know, nobody trusts that. So, you know, it's, it's the redundancy that's built in, you know, are we going to do you know, backups to, you know, to, to our local desktop, or we're going to print out, you know, a daily report, because what are you going to do when, you know, there's a hurricane that comes through retreating, like, what are you going to do, if a natural disaster comes through, I always have my paper, I always have my paper chart, you know, but there's that trust and what you can't see. And you know, we're all used to the internet going down the Wi Fi going down. But as an embryologist, you still have to do your job. And if everything is up in the cloud, and you come in, you got no Wi Fi, you know, how do you know what patients to do the first checks on or how do you know what patients to, you know, to do the freeze on or which embryos to thaw. So, you know, we do need to get better at that, you know, ensuring you know, what we're going to do from redundancy standpoint, to be sure that those concerns are addressed. And, you know, I think is, is, you know, manufacturers out there, we need to play a bit better in the sandbox with each other, and, you know, working on ways to get these systems communicating better with each other, because each one, you know, is kind of fine on its own, but there are these own little islands that aren't interacting very well with each other. They're very clunky, you know, not not not very quick. So, you know, we do need a lot of development still in those areas. But and I think, you know, the only way is to have kind of testing labs, you know, where where we can kind of kick the tires on these things and bring embryologist in to use them?


Helena Russell  34:40

Well, just to add to the you know, a lot of what we see in other industries, like the banking industry, a lot of what they do is done in the cloud. And you know, they have to have their very, very strict rules and regulations and other health care branches of health care industry. These people are doing a lot of commerce in the cloud, a lot of data storage in the cloud, and those redundancies have to be backed up by a robust IT support system. So they do exist for some of the systems that, you know, we've been talking about, you know, sort of loosely, but the really good ones are going to have that kind of support and structure so that you can, you know, assure those who are using it, hey, that information is going to be there. And they have to have an offline, you know, like a holding place at their own facility, a server that that information can be stored on,


Eva Schenkman  35:36

I still see a lot of doctors practices, their servers are in a closet down the hall. Yeah, and, you know, a lot of clouds. Yeah, that, you know, and, you know, we don't really hear it's not really openly discussed, but you know, we get a lot of clinics, there's a lot of clinics that are hit with ransomware. And, you know, a lot of that is kind of kept swept under the rug. And that's something that we need to, you know, why why do we not have a strict regulations as the financial industry, as far as how we're keeping this data, you know, where we're keeping this data redundancy,


Helena Russell  36:05

if you're thinking about automating, and you're thinking about going down this road with an EMR ask the really important question. And that is, how is this stored? What is your security structure? How is it done and who's handling that? Because, I mean, you have to, you have to have a very robust system, and it has to be redundant, can't just be stored in one place and must be stored in multiple places. And how that is done is actually critical, not only to the, you know, the security of your data, how you trust your data, the validation of the systems, but also whether or not you can move forward and practice one day, you know, if somebody holds you for ransom, you're stuck.


Griffin Jones 36:47

Well, that solves for the issue of redundancy, it solves for a lot of the issue of implementation. But a lot of what you described is still the challenge of delivery versus operations. A lot of the reason why people have their server in a closet down the hall is because they've been so busy fulfilling delivery commitments, meaning seeing patients doing retrievals doing transfers, and all of the lab work on the other side of that, that they have not had the time, money energy, to focus on the overall operation systems, you happen to have a program that takes care of a lot of the risk that allows people to visit allows people to do this without putting their own things at at risk or and taking their own, you know, having to test everything within their own system. But they still have to say, alright, well, I've got you know, maybe I've got four embryologist and I need seven. And so how am I going to send you one of my foreign biologists when I'm already half staffed? And, and so how do you how do you begin to solve for that


Eva Schenkman  37:56

one of the things we've been doing is offering you know, several, kind of intensive lengthy courses a year, you know, we, we, you know, and Elena primarily has been going out to to the universities we have someone who's also worked with us doing you know, on tick tock, you know, doing tick tock videos of getting those students out here to, you know, for training, so they typically come to us for for 10 weeks and we teach them everything from Andrology to biopsy, you know, we don't expect that these these these, these new embryologist could go back to their clinic and you know, be doing biopsy on day one. But you know, the typical in the old school apprenticeship style, it would take between two and four years to train one embryologist then we're losing embryologist at a much quicker rate than we can replace them. So if not only, you know, the training school that we have, but the other ones that exist in the country. You know, we are we believe that we're able to now get that training, once they're at the clinic down to under 12 months, so that we can speed up their training. So if you've got four you need seven. Well we can send you you know, you know, we're churning out embryologist, every embryologist that has been through here. I know everyone else had been through, you know, the, you know, one of the other firms California has had a job offer, you know, they're all you know, getting employed. And you know, we need to to, you know, bring through more embryologist and you know, and replace somebody even even a faster clip and that's the only way you know, we can't any longer do this, this apprenticeship, where it takes two to four years to get one new embryologist it's, it's not it's not sustainable. You know, we need a better way of of bringing them bringing them up, bringing them through quicker getting them trained. And you know, the style that we do it here which is very intensive, you know, they spend probably close to about 500 hours, you know, doing every literally every procedure and you know, over the course about two and a half months,


Helena Russell  39:52

hundreds of times they do each procedure hundreds of time. So what we're doing is set adding them up to make it easier for those who are doing the training on site in the IVF lab, making it easier for them to get the embryologist they need. I do think that part of the operational pushback is there needs to be kind of somebody who could bundle I really do believe that there's a there's another role out there for it, an IT biologist or something, you know, somebody who could go into a lab and do a consultation and say, you know, an EVA really has that kind of perspective, she may not be the IT expert, but she has, you know, a really good perspective on, you know, hey, you're doing this, this, this, and this, here are some products and, you know, we can put all these things together and deliver them to you. And you know, here's our IT redundancy expert, you know, can come in, look at your system right now, and say what needs to happen? And what tools can we bring in here that are going to meet your needs? What need do you have? Do you want to do all your quality control remotely? Do you want to do your embryo analysis remotely your embryo culture analysis remotely? Do you want to bring all your data together so that you can meet your KPI with a click of a button, review your your KPIs, and then bring all of those things together, and act as a liaison between all these different groups? Because it is a little mind boggling when you look at what is happening in the IVF field. And you have you know, this automated system and this automated system and this automated system and this automated system, how do you bring all of those things together? That's the challenge. And not everybody's going to want all those things. So how do you do that? That's that part of that operation could be someone who's an expert at all these different things, helping to give advice, consulting, and charging a fee to bring it all together for them and stitch it together.


Griffin Jones  42:01

Helena, you were talking about the challenges in having so many different automation solutions, one solution to that problem of having so many is having a consultant or an umbrella solution of some kind that can bring them together. How much of the problem is also those solutions not integrating with each other not integrating with the EMR? How common is that


Helena Russell  42:28

it's happens all the time. And Eva spoke to that earlier that people in these different realms need to play well in the sandbox, they need to be able to open up their their systems a little bit, so that they can speak to each other push and pull data, because a lot of times you'll see, well, one company will let you do one thing, but not the other. And you need both. And, you know, I think it's a little that's an operational hurdle. And again, an integrator, somebody who really is quite savvy and knows, you know, how to communicate with these folks could hopefully bring some of this together, I know of, you know, at least one company who's doing things like that. I'm sure there are plenty of others that are attempting that, you know, it's it's a daunting task, we know that we know it's very difficult to change. But one of the things that the light at the end of the tunnel, you're never going to stop changing. And IVF though that's just plain and simple, it, you're not going to reach a pinnacle and say, Oh, we're done. Now we've reached the pinnacle, because something new is going to happen down the road, something new, some new way of doing analysis. And so you're going to always have to change you're going to have to learn to live with that. And like Eva has said some of the newer generation, they're used to maybe looking at things a little differently, maybe not so much always changing. But at least the electronic aspect of it doesn't seem like it's so that was daunting, not as daunting not as as much of a trust issue. Now I can't trust my computer gets viruses, right, or I can get malware. So I think that, you know, if you if you have the right systems and the right checks and balances the right security systems and redundancies, as we've said, you will begin to you know, get over that hurdle. That's one of the biggest ones.


Griffin Jones  44:20

But if they don't integrate, aren't we back to the same challenge of the spreadsheets?


Helena Russell  44:25

A lot of them are integrating. Yes, we are if they don't integrate a lot of them are seeing the handwriting on the wall. I think Eva, wouldn't you agree?


Eva Schenkman  44:35

I think so. Now,


Griffin Jones 44:37

seeing the handwriting on the wall and that they're not being adopted, if they don't integrate


Helena Russell  44:42

They’ve got to make themselves a lot more malleable in order to be adopted. Like you just said, if if we're trying to show people how to use a KPI and the system that is is giving you your best data and is not you No handing it over that you have to actually export it and upload it a different way that may be not as user friendly, you might do it. But if somebody else down the street will integrate, guess who's gonna get pot?


Griffin Jones 45:14

So there might be a market response that forces people to integrate more you had in the beginning of the conversation, you alluded to some solutions, maybe not coming to market, but not having the scientific proof that they have a great benefit. What are some examples of that?


Helena Russell  45:36

Well, I think even would agree that there are some products out there that we need to more closely scrutinize and names. I'm not going to do that. But I will say that their artificial intelligence base, but the the issue with some of these is, you know, the gold standard in scientific medical research is the randomized control trial. And some of these products, they may have them in progress, but as far as I know, not really have published as much as they should, or at all. And so one of the things that I think we need to as a scientific community, which is what IVF is a part of, is that before we fully buy in, or spend an awful lot of money on something, that I mean, maybe we volunteer to be part of that study, you know, if you're an IVF center, and you're interested, you know, say, okay, all I'll be part of this study in order to help advance this field so that we'll know one way or the other, what they're promising may not be that we have better outcomes, necessarily, but that we might have more efficient outcomes, which might lead to better outcomes, because maybe your embryologist won't be so incredibly stressed out all the time, because they can't function because they can't get all their work done. Because there's not enough of them. And this automation could become part of the workflow that holds an answer for them, at least part of an answer.


Eva Schenkman  47:13

And I think that I agree with Helena, you know, the biggest issue is, is you know, especially, you know, right now, you know, the flavor of the month is kind of anything AI. And you know, each of them have some some papers coming out that they're showing that that, you know, this system is the best or that system is the best. But there's really a lack of well, plans. Well, well, rigorous setup. Yeah, what very rigorous those randomized controlled studies. And that's really, because what happens is people that adopt it, and they don't see the same benefit in their hands. So there's a big distrust of it, when you have for profit companies, who are then also sponsors of these papers, we're putting out data saying that this is the best thing ever. And then when somebody pays the money and adopts the system, they're not seeing, you know, the same, you know, Return, return to there. And so, you know, I think, you know, that's probably the one thing in this field that that I think is hurt us that we don't do, you know, as many well planned RCT studies, that, you know, we do a lot of retrospective, a lot of, you know, prospective, but not necessarily a gold standard, you know, stuff, which is hard to do.


Helena Russell  48:22

I mean, in IVF, it's very difficult to do that. Now, it's very difficult to do certain kinds of randomized control trials, because you do not have, you know, that many chances for fertility, in many cases who are coming to you for treatment. You know, if you're going to do a randomized control trial, it's got to be planned in such a way to limit the harm or potential harm for the patient. What's harm harm is, maybe they didn't get pregnant. And so, you know, in these cases, when you're looking at artificial intelligence, as long as you have a good check and balance, like you're having, you're having your own technicians review, and re and, you know, respect what's coming out, but review what's coming out of the AI. And make sure that well, whatever it is, it's telling you, you have the human aspect that you've learned to, you know, know, you know, and love, and you trust, then, you know, oversight is good, but what does randomized control trial mean? And what is blinded mean? Because a lot of times bias, unfortunately, you know, enters into these things and how do you create a study where there's limited bias, meaning that you're not overtly influencing the people who are conducting the study? The doctors, the even the patients, and certainly the embryologist, how are you ever going to blind the embryologist? Probably not never, you're probably never going to blind them because they're going to have to keep the numbers straight. Somebody has to protect the patient's embryos and make sure they really truly understand they know this is embryo 1234. And this is embryo 3456 and make sure everything is working properly. So blinding, the embryologist is almost impossible.


Griffin Jones 50:07

Which RCTs? Would you like to see happen with regard to AI companies entering the lab space? Like, can you detail what you would like to see an RCT or a couple of RCTs?


Helena Russell  50:18

I mean, even you talked about this the other day with the AI that you were thinking about that, that I think one of the things that we need to see is more numbers, also consistency and how the training database is working. So how you build that artificial intelligence is by having, you know, a large enough number of input and outcomes, you know, so you have something that you're observing, right, and you're applying an algorithm to it. And then what comes out the end is, hey, do it this way, or, or select this embryo. And so if you have a large enough database, you could potentially apply that one of the biggest problems that we have, is applying it across the entire world, probably not doable, because in each and every lab or each and every IVF. Center, there may be some variables that we really have no control over, that we have to kind of focus in on that particular lab and having enough data to have an artificial intelligence algorithm built may not be possible on a center by central basis. So some of these things, I think it takes time to develop the algorithm and then apply that to a randomized controlled trial, where you're looking at either isolating the artificial intelligence and doing it with sibling embryos, for example. So you have to have a special population of patients who have enough embryos that you could put them into different systems and compare them, or potentially looking at, you know, larger populations, if you don't have those sibling embryos to look at, you could look at groups of individuals in those two different, you know, isolated, different ways of producing the embryo, for example. So it goes beyond what we're currently doing in the lab, which is observational, when we even when we look at time lapse imaging, we're looking at changes over time that those are very interesting markers. Because you could see slow development versus fast development versus abnormal development. And you can see all that in a time lapse imager, this is something that you could never see as a, the traditional way of analyzing embryos to pick for transfer is a, you know, a one, a particular time point. And looking at an individual, you know, time point is, is not as superior as looking at, you know, time time points throughout the developmental process over the five to six or seven day period, that we have them in culture. And what Eva's talking about is even more specific and more precise. And that is going after those molecular markers, where you look at gene regulation, you know, those kinds of subtleties are almost impossible to you may not see anything, but and they made the embryo may be developing perfectly well, you know, it's just looks like a normal embryo. But when you actually look at the molecular profile, and look at the genes that are upregulated or downregulated, compared to the perfect environment where you can't replace something like that, you know, and and in past times, some of the things that people have looked at are metabolomics. I don't know if you've ever heard that word, but it's okay, the embryo is growing, and we're looking at metabolites of growth, and you siphon off some of the culture fluid and you look to see oh, is it metabolizing? Well, but actually looking at gene regulation, and and looking at markers that are very fine detail of the health of an embryo could be a potential answer.


Griffin Jones 54:15

I appreciate you both giving these so much insight into the different obstacles that are inhibiting automation from fully taking the IVF lab by storm. How would you like to conclude with regard to what needs to happen in order for automation to take its full rightful place in the IVF lab?


Helena Russell  54:37

I think what we need to do are some very detailed studies, where we look at how the impact of these automations on you know, first adopters, you know, there's always going to be a group of people who say, I'm there with you, I want to go automation all the way I want to do these things that are going to assist us in in prevailing and thriving and And moving forward, those first adopters should be studied. And efficiency should be studied, we should study all aspects of, you know, their turnaround time for troubleshooting, they're, you know, catching things on the on the fly when there's a, you know, a detail that's out of place for their QC, their daily Qc is messed up and they get an automated announcement. And, you know, there are people who are malleable to this, you know, they will be early adopters. And so those are the folks that we really need to study we need to present at meetings, we need to maybe create the perfect training environment like we have here at Art Lab, where you can bring people in, expose them to this integration and say, Okay, this is how it could work in your lab. You show them something, and that barrier is may not be eliminated, but it's gonna come down a little bit.


Griffin Jones 55:55

Helena Russell. Eva Schenkman. Thank you both so much for coming on inside reproductive health.


Sponsor  56:01

You've been listening to the inside reproductive health podcast with Griffin Jones. If you are ready to take action to make sure that your practice thrives beyond the revolutionary changes that are happening in our field and in society. Visit fertility bridge.com To begin the first piece of the fertility marketing system, the goal and competitive diagnostic. Thank you for listening to inside reproductive health

179 Chat GPT Has Arrived In REI: Conqueror Or Collaborator? With Dr. Ravi Gada and Manish Chhadua

DISCLAIMER: Today’s Advertiser helped make the production and delivery of this episode possible, for free, to you! But the themes expressed by the guests do not necessarily reflect the views of Inside Reproductive Health, nor of the Advertiser. The Advertiser does not have editorial control over the content of this episode, and the guest’s appearance is not an endorsement of the Advertiser.






Please Note: We recorded this episode two months prior to release, and Manish and Ravi have already been pinging me about changes that have happened since. I will do another episode with them because this topic is constantly evolving!


Chat GPT is here to change the future of your job in the fertility industry, or maybe even take it. Is this a stretch? Dr. Ravi Gada and Manish Chhadua discuss how Chat GPT and its predictive technologies has the potential to revolutionize is already revolutionizing the fertility space. And what may come next.


Tune in to hear:

  • Uses for Chat GPT in fertility clinics and the Open AI source behind it.

  • How Chat GPT is being used to share data with patients, aggregate data, how it may be used in the future to generate prompts and consult notes.

  • The elimination of scribes and schedulers.

  • How Chat GPT will be able to interface with patients to provide 27/7 availability and access to care.

  • Griffin push Manish and Dr. Gada about what the second and third order consequences will be from this development, and what significant long-term impact it could have on the future of REIs.



Dr. Ravi Gada:

LinkedIn: https://www.linkedin.com/in/ravi-gada-md-mba-a2307527/

Manish Chhadua:

LinkedIn: https://www.linkedin.com/in/mchhadua/
Website https://reuniterx.com/




Transcript


Dr. Ravi Gada  00:00

In the fertility space, what we're going to deal with is who owns the data inside the EMR. So, when we talk about regenerative AI and language modeling, we're talking about being able to talk back and forth with a patient, maybe summarize a chart, create a summary of a consultation and put a note in the EMR. But we also talked about in AI, this whole idea of helping predict outcomes for IVF, as well as dosing for medications for a cycle embryo growth and development and who owns that data.




Sponsor  00:31

This episode is brought to you by Univfy, email Dr. Yao at mylene.yao@univfy.com, or just click on the button in this podcast, email or web page for your free IVF artificial intelligence tips and strategies. Today's advertiser helped make the production and delivery of this episode possible for free to you. But the themes expressed by the guests do not necessarily reflect the views of Inside Reproductive Health, nor of the advertiser, the advertiser does not have editorial control over the content of this episode. And the guest's appearance is not an endorsement of the advertiser.


Griffin Jones  01:13

A monkey can do an IVF egg retrieval. That's something that more than one REI has said to me. That's a euphemism. That's not really true. But will we be saying that what Rei is can do today is like the intellectual capacity of monkeys, based on what's coming with artificial intelligence? That's the topic of today's episode, you might listen to today's episode and wonder is Griffin high? No, the topic of today's episode is exactly why I don't get high. I talked to Ravi Gada. Dr. Ravi Gada and Manish Chaddua. Both of Reunite RX niche is the founder. Dr. Gada is their medical director, Dr. Gada also practices at Dallas Fort Worth Fertility Associates, we talk about chat GPT, which many of you have heard of some of you may have not the open AI source behind it, we talk about the applications that it's having. In the greater context right now the applications that it's having in the REI practice, how it's being used to share data with patients, how it's being used to aggregate data, how it will be used in the future for prompts and generating consult notes, how it will replace the work of scribes and schedulers and nurses how it will be able to interface with patients as an avatar of you. Because of technology that already exists. Today, I pushed Dr. Gada and Manish To explain what they think the second and third order consequences will be from this and what the REI will do when half of the communication half of the tasks that they're responsible for today are done by artificial intelligence tomorrow, at least half depending on what length of time we're talking about. And if we're talking about a long enough period of time, does it become everything that an REI could possibly do in a way that they couldn't possibly add any more value over what general artificial intelligence can do? You'll notice throughout this conversation, we really tried to keep the conversation about the applications of what happens in the REI practice, at least for half of the episode. But there's almost no way to contain it to just that open AI is Chat GPT product is just the tip of the iceberg and it has implications for every single aspect of the human experience. I might sound dystopian or pessimistic when I'm trying to get Manish and Ravi to think about this during our conversation. I don't think I am I think I'm pretty neutral. I'm not making a value judgment if it's good, bad or neutral, but follow along as we discuss how this conversion of technology not only replaces workflow that happens in the REI practice, does it replace the concept of human production altogether. Buckle up. Don't even consider consuming anything that has cannabis in it and enjoy this conversation with Manish Chadduaand Dr. Ravi Gada. Dr. Gada, Ravi. Mr. Chaddua, Manish. Welcome to Inside reproductive health.




Dr. Ravi Gada  04:06

Good to be here.




Manish Chaddua  04:07

Nice to meet you.




Griffin Jones  04:09

Manish , do you know how many times Ravi has Monday morning quarterback my show and I get a text or an email something that I should have said or something I should have asked. I've always asked him to come on. He says no, I don't want to rock the boat. I don't want to shake salts. I don't want to stir the pot. And finally I got a text from a couple like a month or two ago saying okay, I got a topic let's talk about yet. GPT. And I said all right, great. This'll freak people out. And he said companies government I said Yeah, so I want to freak people out about chat GPT. But we were speculating before we even started recording how much of our audience knows what chat GPT is how many of them know about open AI the platform that it's built on? So why don't we start Elementary and just give context for what we're even talking about? 




Manish Chaddua  05:00

I think a lot of people have read a handful of articles maybe about chat, GPT. But you know, it's an endeavor that kind of started probably about five years ago. It's often invested heavily into it. And then, you know, really just back in November of this year, last year, they basically launched this first kind of forward-facing view for consumers of what exactly it's capable of. And so the founders behind it are, you know, a handful of guys, Sam Altman, Peter Thiel, Elon Musk, I think are some of the original core for it. But since Microsoft has invested upwards of $10 billion into this product,




Dr. Ravi Gada  05:38

well, and Griff just, I don't know if people know what I mean, Sam Altman is the former CEO of Y Combinator, Peter Thiel, former founder of PayPal, Elon Musk, obviously everybody knows. So it's got some pretty big backing behind it.




Griffin Jones  05:54

People know those names but tell us about what Chat GPT is doing.




Dr. Ravi Gada  05:59

Chat GPT is an AI language modeling platform, it's probably considered a SASS platform where users can go onto the web, create a login, it's absolutely free to use, there is a paid version of it that you get a little bit more priority. And you can ask it just about anything. And it has over 100 billion different data points. But you can ask it, you can just talk to it. If you're like, Hey, how are you today and go through a conversation, you can ask it? What's the reason for having an Hmh of less than one, you can ask it to draft legal documents that you can ask it to write a poem. So and really, it puts this together and you can iterate on it back and forth to get to the point where you're happy with the answer, copy paste, but it into your platform and use it a lot of people are saying it will be used to augment the workforce and make our lives easier.




Griffin Jones  06:54

Manish, How does that work? Like how is Chet GPT using open AI to be able to do that?




07:01

So chat, GBT is called the term that's being used for it as generative AI. And so what chat, what they've done is they've basically created, you know, in the term is a caucus of data of about 170 billion data points, which is articles, publications, all sorts of data points across the internet, they stopped collecting that data in about 2021. And really, the way that it works is actually through algorithms and just math, it's predicting the probability of the next word or the next most likely word in how it's generating this text. And so that's kind of the clever thing about it is that it's this large, large data set, it's able to basically look at that data set, and then predict the profitability of the next word. And that's how it turns into the text that gets outputted when you're asking your questions and the context that it actually receives when you follow up with that question, and things like that. So it's a predictive model,




Griffin Jones  08:01

Doctor Gada, give some of the use cases that Chat GPT is being used for what are some of the funky ones that you've seen, one of the funky examples that I've seen was, like, talk about a certain type of story in the tone of comedian Tim Dylan, and it was the comedian, Tim Dylan reading it. And it was pretty close. And even he says, like, wow, this is, this is pretty close. And it clearly wasn't there yet. But it's more than just write a poem or write an article, you can actually say, write an article for this certain type of audience or write it in the spirit of x. And so what are some of the wacky examples that you've seen?




Dr. Ravi Gada  08:43

People are predicting this year, chat GPT, or any other language modeling system is going to write a screenplay for a movie, it's going to give it some input data on what type of movie at once and who the characters are, it's going to write the movie from start to finish finish. And they're going to take that storyline and put it into an animated AI platform dollies for pictures, but there's some animated ones in the background, and it'll create the animated movie and that by the end of this year, we'll have a movie in which the screenplay and the animated movie are all done by AI with minimal human input. Wow. So even




Griffin Jones  09:21

the characters, the action of the animation is going to be created by artificial intelligence.




Dr. Ravi Gada  09:27

Yep, completely based on the language of the screenplay, and it'll make all the action of the characters, the voiceover to voiceover as well. So you can there's voiceover you can do now, so I could probably record all of your podcasts, uploaded it to chat, GBT right what I want Grif to say and replay it, and it's going to sound like I'm doing a podcast with you. And we can call it something else.




09:48

Well, I'm going a step further from that they can actually model based off of a handful of pictures of you an actual animation of your face and have that talking as the actual animation for that. Voiceover so that's so they can mimic like real life people and things like that. And that's not just GBT. But that's other AI solutions that are out there.




Griffin Jones  10:08

Sure. What is that? Is that the deep fake? What is that?




10:12

It's related. I mean, it's in that vein. Yeah, exactly. Yeah.




Dr. Ravi Gada  10:15

Deep fakes, probably the most popular one.




Griffin Jones  10:17

Is that a different type of artificial intelligence? What's behind that?




10:23

Yes, sir. I don't know a whole lot about what's exactly behind that. But it is using AI to basically evaluate facial expressions and things like that, like deep fakes, specifically takes my facial expressions, and it superimposes your face onto it. There's other versions of that that basically will just take text and known kind of vernacular and how mouths moves and things like that, to basically create video or animations of a person actually talking.




Griffin Jones  10:51

Okay, well, I could just totally dive into this part where I'm deeply concerned about someone making a podcast episode.




10:59

That's a really weird edge case, or not weird, but just kind of scary, is that even hackers are using chat GBT to generate clickable content so that way, they can send email blasts out and they'll just ask it things like, hey, create a email that's basically has a link in it, that's the most likely to be clicked by users. And it'll actually generate and so this is another edge use case where it's like, okay, well, you know, the malware the ransomware type of folks out there using it to help move their cars.




Griffin Jones  11:32

Well, I want to come back to this and talk about what we think second and third order consequences might be of all this, but let's talk a bit since this is, after all, a show for Rei is it isn't Rogen were talking to fertility specialists and the people that own fertility practices? What are the applications that open AI can be used in the REI practice at this time?




Dr. Ravi Gada  11:59

So I think at large, right, we've, we've seen in our space companies that come in just using AI for data mining for embryos, look grading eggs, grading embryos, there's companies trying to predict what's the outcome of an IVF cycle. But we haven't really seen too much movement in the linguistics modeling or the language modeling. So in an REI practice, could you create a chatbot that basically communicates back and forth with a patient answering simple questions. So if a patient calls, or has a question about what's my Hmh level? Or what's this thyroid function test, could could a language model reply back and forth with that patient just enough to answer as many of their questions as possible? In healthcare, you want to be very careful in what we call follow up criteria. So if the if the bot doesn't know the answer, then say, Hey, let me get one of the nurses for you or one of the doctors and then someone picks up the conversation from there. But you could think about that in a way where patient has free access or 100% 24/7 access to a chatbot that's been trained by us in the REI community. We've given all the language the data points, the conversational pieces to have. So that's a use case. Interestingly, I did a did a thing the other day I put write a male male couples gestational carrier contract, and it spit out a gestational carrier contract immediately. And then I said, Well, can we add language for what happens in the first trimester if there's abnormal screening, postpartum does the gestational carrier provide lactation and milk for them and and it added all these sections in there along with by the way, an exhibit page to add the financial conditions of all of these things, so I can have it write contracts for third party reproduction pretty easily. I had a patient asked for a work excuse the other day, and I had chat GPT write a work excuse after an abdominal myomectomy for six weeks, and it wrote it for me. It leaves blank so you know template so then you copy paste it and then you add the patient's name, sign your sign it and send it.




Griffin Jones  14:12

Let's talk about the EMH level example for a moment, the thyroid function example for a moment, how would we know if the bot gives the wrong answer?




Dr. Ravi Gada  14:21

So this is the part that gets complicated, right? So what's interesting is there's plenty of companies today that have language modeling, ai, ai ai, so chat. GPT is owned by open AI, open AI is primarily going to become a Microsoft based company. Recently, Facebook launched one called llama and then Google launched one called Bart and so everyone's going to have a version of this. You have to then take their AI language modeling and input your own data set. So perhaps that's recording the next 1000 hours of calls with nurses and physicians with their patient. inputting that data. And then running tests to see is it doing what it's supposed to be doing? And if it is perfect, if it's not, you have to give feedback to the system always. And that's how it's why it's called machine learning or regenerative learning is it has to learn from itself. The patient either has to tell it, it's wrong, the nurse has to tell his strong, but you've got to feed that system enough to be smart enough to give the right answer and smart enough to know when not to give an answer. But that's going to be the biggest challenge in our field is making sure it doesn't overstep its bounds.




Griffin Jones  15:33

And so at what point do we expect it to be able to be a better judge than a human being?




Dr. Ravi Gada  15:41

I think in some cases, we might already be there in certain language modeling. I mean, when we in you open up your Gmail, or Outlook, and it practically finishes your sentence for you when you're typing up an email now, and sometimes I'm like, well, that's better than I would have wrote it. So let's just go with that. But in the healthcare space, I think we're I think we're a bit of ways I think we always are later adopters, for new technologies for that reason. But if I had to guess, I mean, we have to be three to five years from being able to really, I hope within three to five years, where they're where we can leverage this type of technology.




16:14

And the biggest challenge is going to be what Robbie's talking about this Fallout criteria. So when we think about AI, and basically, you know, creating the answers are basically predicting what the answer should be. The probably the, the hardest part is going to be that aspect of just knowing when not to answer and AI is not there yet, or doesn't seem like it's there, which is why a lot of stories are online about how they're tricking chat GPT and providing wrong answers to math questions or, you know, doing a handful of other things like that. So that's probably going to be where, you know, some, the physicians in general, will view this as a tool that helps them get to the answer faster. But it's still, you know, we're far away away from between us getting to the point where we can blindly trust that to do that.




Griffin Jones  17:06

Have you read anything about the regulatory bodies or the agencies thinking about how we're going to regulate this either from ama or from Fe cog or from is anybody talking about this? Rob?




Dr. Ravi Gada  17:19

I don't think anybody's talking about him. I was listening to a couple of podcasts about it. So in healthcare, it's not interestingly Moniece mentioned to me earlier today, chat, GBT did certify that their HIPAA that they have a HIPAA compliant API version to it. I don't think any of the society organizations are talking about it. Even in this sense of copyright. People haven't really quite figured out when chat GPT pulls language from the internet, essentially rewrites that language and spits out an answer. It's not giving attribution for where that came from. And so there's even concern that could chat GPT ultimately get stuck in lawsuits with copyright? And are they just rewriting someone else's language or or verbiage that's out on the internet without site citation of credit? And Google does it right you type a search? It gives you an answer. But there's a link to where it goes from they might summarize a little bit in the in the description part. But ultimately, it gives credit through a link which chat GBD does not. So there's some people looking at this, but I mean, no society organizations from a medical standpoint, no, I don't think anybody is even digested what this technology means




Griffin Jones  18:31

until they hear this podcast. And they're like, Oh, crap, we have to have a board meeting.




18:36

And one of the counter arguments to the copyright thing that Ravi just brought up is that, you know, do humans in general do anything different? Are we just basically absorbing information and data from a variety of sources, and then basically mimicking what we hear with some amount of, you know, how much innovation is actually being produced? Out of what we regurgitate? Right? Some attribution




Griffin Jones  18:59

and some innovation, but very often isn't even possible to totally attribute everything because like the machine, you might be saying, in this case, money's we're aggregating and it's an amalgam of everything that we've consumed. But I was I was going to ask you that question about intellectual property, too. And you brought up the example of Google Ravi. And I wonder if if case law is still been established about that? Because sometimes I think like, is that enough when a creator is putting out information or creating something, and Google just kind of takes it and they put it on a Google search? And yeah, they give it a little bit of credit, but very often, what does the Creator actually get from that credit? If that person gets their answer right there in the search, they don't ever have to go to the creators website. They might see that little URL at the bottom, but they're pretty much just getting their answer from Google. Is there any kind of case law that, you know, Manish that has been established? Or is there are there battles going on about this




20:07

definitely is something that's been brought up even just about how the way Google works. Now Google gets a little bit of away with it, because they are actually providing that attribution. And I think that's where chat GPT will be very different. Because, you know, it's not the Texas generating is somewhat unique, but it's not actually sourcing that direct place of where the data is coming from. Even Ravi and I have had conversations about this as well, just to say, you know, here are the different differences. And then, you know, Google is a little bit different of an animal as well, because it's giving that attribution, it's giving hope to those creators to actually get the clicks or get the referrals. So I think it's a little bit of a different scenario altogether.




Dr. Ravi Gada  20:48

But there, but there is, there is case law for this. So there is something called fair use for copyright. So fair use has been established that our case law underneath that there's four criteria for violating fair use, but one of them is not citing the person, but it has to affect their ability to monetize. So if you have a company that has a bunch of articles about fertility, and you're regurgitating their data and putting it there, and their whole business model is to get links, have people click on that? And then ultimately buy something or lead them down something, then? Yes. And that's where Google pays people for that link. And so there is it's called fair use. I mean, I don't know that it applies to copyrights. Specifically, it's not going to apply to what we're going to deal with in the fertility space. I think in the fertility space, what we're going to deal with is who owns the data inside the EMR. So when we talk about regenerative AI and language modeling, we're talking about being able to talk back and forth with a patient, maybe summarize a chart, create a summary of a consultation, and put a note in the EMR. But we also talked about in AI, this whole idea of helping predict outcomes for IVF, as well as dosing for medications for a cycle, embryo growth and development. And who owns that data? Is that the patient is that the clinic? Is that the EMR? Is that everybody? And I think there will be a little bit of information that comes out from probably not the fertility space, but probably more on a higher level of internal medicine or diabetes of who owns this data.




Griffin Jones  22:27

I wonder if this affects people like me even more so than it might the general public and that those that are in deep niches, and are based around information are in deep niches, part of the reason why anybody picks a niche, whether it's a client services firm, or media company or software company is so that they're delivering specific needs to a small group. And that's where they that's the entire reason why they do it. And if something can just say, hey, take everything that inside reproductive health has gathered and created from original sources, then it could be the small niche companies that are most vulnerable, don't you think?




23:16

Yeah, I mean, content creation is something that's going to transform quite a bit. I mean, even if you look at the way, you know, traffic gets generated, and Google and even beings a algorithms work right. Content Creation is like, been the pinnacle of how they judge what's what's good, what's not what's new and fresh. And so that's definitely a large area of impact. I mean, there's, there's sub companies from chat GPT that have already been created that just create copy, and they create everything from sales, copy, marketing, copy, blog copy. So that's definitely distinct part of I wouldn't call it a threat, but a possible, you know, a rethink of that approach of copy creation or content creation.




Dr. Ravi Gada  23:59

I think the niche markets will get saved in this because when I look at health care, people focus on cancer, diabetes, hypertension, obesity, and fertility, and very small sectors get overlooked. And so all of these companies I think, are going to be focusing on the big three, you know, in terms of hypertension, diabetes, obesity, and then add cancer, and infertility kind of gets overlooked. I think that's why actually, as a field, I feel like we're very technology deficient. We don't have enough technology infused into this space, and maybe will be saved. I don't know.




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Griffin Jones  25:57

We talkedabout a couple of the applications that you're using right now what applications do you expect that aren't quite there yet, but that open AI chat GPT will be able to do in less than three years.





Dr. Ravi Gada  26:11

Imagine a day that we're I'm sitting in a consultation room with a patient, there's a TV screen behind me here. And I say well, let's take a look at your Hmh level today. And on the screen, it hears me say that and pop to the h a m h up on the screen behind me for the patient to see that. And then I say, you know that's numbers normal, you know, that should mean that you have a good ovarian reserve. We also do a follicle count to look at that. And here's me say follicle count from your ultrasound. And it puts that up on the screen. And I have this now interactive conversation with the patient. They're asking me questions, we're going back and forth through a return visit or new visit. And at the end of that visit, we walked out of the room, I hit done on the recording device. And it generates the entire consultation note immediately on its own because it's regenerative language modeling gives me You know, I can then sit at my desk take 30 seconds to read it finalize it done, by the way, any edits that I make to that note that I didn't like the way it writes, it's recognizing that I edited it and and learning from that. So I think at the highest level, you could look at that you could look at it basically, you know, every six months, every three months, it reads the entire chart for a patient and summarizes it in a note on a three month update or a weekly update depending on what cadence you want to do that in. So there's things like that there's things that I could have it recording all the calls that my nurses do to patients, right, I rely very heavily on my nurses to communicate back and forth with patients. And I can and the language model can tell me if there was inaccuracies being presented or something that is different than what I would have said based on its understanding of the conversation. And then we can we can retrain that nurse, we can improve things, you know, it goes beyond nursing, to imagine the day that all of these things are just used as tools to make us better, more efficient. And ultimately, it will probably take over half of the I wouldn't say conversation but communication that we have back and forth with our patients.





Griffin Jones  28:24

At what point might we expect to see the avatar Doctor Gada, having that follow up. And so if all of those things are just different data points, and it can compare it to all of the data points from every piece of scientific literature, fertility and sterility is ever covered. And everything from all of the medical colleges, if it can just deliver that type of information, and we can use your video we can use your voice at what point are patients just seeing a virtual Doctor gotta





29:00

so I think the humanity and US will fight that pretty pretty well. So I think if you look at telemedicine, a lot of things like that, I still think the preferences is face to face communication, I don't think you can replace that for some people. Right. And I think for places where we're underserved pay at places where we're trying to get into that aren't getting quite the availability of health care. I think those are the areas where you'll see this kind of really explode or really thrive is to care for patients in in those particular areas.





Dr. Ravi Gada  29:32

But I mean, I've talked to Manish about this you know we have a lot of pilot projects in this area of where where will this technology take us and how do we get in a lot of it's in the datasets that are fed into the system but when I do think does the day come that you asked the patient would you like to see the human doctor or would you like to see the avatar Doctor initially or virtual care models are already there today. Many patients are going online and wanting to order their her own tests and get their information at home or through virtual care. So I think there's a version of it today, I think there's going to be a more sophisticated version of it in the future.





Griffin Jones  30:10

I'm a little skeptical on Manisha is hope for the humanity aspect. I think people want the humanity when they feel that the robot is insufficient. So the reason I yell into my phone when I'm talking to the the banking teleprompter is because it doesn't understand that I'm saying, talk to an agent or review account balance. But if I actually could do that as easily as I could correspond with a human being, I think it has more to do with convenience than humanity.





30:42

Yeah, for sure. And grip, I think my my point of view on that is more for general, for healthcare, I do think fertility is a little bit different, because of the age of the patient and kind of, you know, the fact that every fertility patient coming through as either a for the most part is Millennials or younger, right? You definitely could see this avenue of I'd rather text with my doctor than, you know, talk on the phone with them, or, you know, have to go and show up at a clinic and actually have that face to face interaction. So I definitely could see that scenario.





Dr. Ravi Gada  31:13

You think about this, there's a YouTube video out there, if you type robotic reenact the Moses of bow, using artificial intelligence, there is a cadaver. So it's a pig model of a robot, taking bow and sewing it back together without any human doing it and it healed intact. And then obviously, they checked it sacrifice the cadaver and checked it. And so, I mean, if we're getting to the point where cars can drive themselves, robots can do bowel reenact the most surgery on their own, we will get to the point where communication back and forth with the patient or consumer will get there. The question is how far right do you get to the point where you just do the intake form? And asking a few questions for clarification? Or do you deliver lab results deliver? Do you deliver positive and negative pregnancy tests and that way? That's the part is how far will it take it? I think it's going to go. If you fast forward 30 years from now, there's going to be a way different version of doing this. The question is in the next three to five years, or while we're all around, how far are we going to get?





32:17

And that's absolutely right. Like you take any technology, any innovation like this, and it's all a matter of a timeline, you assess some rate of improvement, and every tech pundit will say that is whatever the rate of improvement you select, that means at some point in time, you know, the technology will surpass the reality.





Griffin Jones  32:36

m&e, as you said, this has been in the works for some time now the technology behind chat GPT. But it seems like there has been an inflection point recently though, no, like, just how good chat GPT is itself. And then I practice with it. And a couple other like, think of translate for exam I, I don't remember the last time I used Google Translate for language, but it used to suck and not too long ago. And recently, I when we were covering the KKR story for buying ie vrma. And their only media coverage was in Spanish. And I speak Spanish pretty well. I put it into Google Translate to see and it was good. I like almost as good as as a native speaker who had been natively raised in both languages. So what's the inflection point when he's what happened recently?





33:28

Yeah, so this is common, right? This is common in a lot of technology, whether it's the smartphone or the internet, or, you know, even AI. And really, it's a byproduct of technology from 1015 20, even 30 years ago, becoming more accessible, less expensive to use, and basically more awareness, right? So you take smartphones from, you know, back in the late 90s. And they existed, and they had a lot of functionality. But it wasn't until the advent of the iPhone, where it really was the right time in place. And the cost equation made the most sense to where it can actually rapidly grow inside of that. And by the way, my background is telecom. So that's why the analogies there. But then pass that chat. GBT really is the first very consumer facing version of an AI model that showed the rest of the world everybody, including, you know, guys, like you and me, as well as you know, just college students and everybody else in between, right, what the capabilities of AI is. And I do think that AI has been in place for a long time. I mean, it wasn't, it was a number of years ago when AI beat, you know, IBM Watson mini in a game of chess. And this is just that acceleration. And I do think in AI, right, if we look at any of these revolutions that have happened, or major disruptions in technology, you know, it keeps happening faster and faster. And so So I think chat GBT has really opened everybody's eyes to what's capable? And now, all the thinkers and innovators are out there? Basically saying, Oh, I didn't realize we were this far along. How can we employ this as a part of, you know, a core model? Or how do we adopt this and find out what the right solution is that's really chasing this already, and integrated into our workflow.





Dr. Ravi Gada  35:18

And Griff real quickly to add on that. So the inflection point was I don't know if sometimes we will realize Chat GBT launched in November of 2022. So the inflection point was the first real launch of a major language model. And it obviously caught fire. And that's why we're all talking about it, or a lot of people are talking about it, interestingly, in that, but it was founded, I think, in 2019, four years, something like that about four years ago. And they've been working on it up until now, interestingly, post chat GPT launched, let's call it circa November of last year 2022. That put a lot of pressure on Google and Facebook to launch their versions. And so Google launched Bart, and they did a commercial about this. And in the commercial, Google asked, or someone asked the chat bot, to tell them about the James Webb telescope. And it was listing some bullet points. And the last bullet point said that the James Webb telescope was the first telescope to take a picture outside of our solar system, which was actually false, it was actually not yet planet and people picked onto that. And as soon as it did, Google's actual market cap value dropped by $100 billion that day, attributed to this error, because everybody said, their language model and their regenerative AI is not as good as Microsoft's, and they're not ready yet. And it lost some around seven to 10%. Market cap $100 billion because of that, but I think chat GPT launching in November is why we're at that flexion point today,





Griffin Jones  36:52

to the point that is a can take over half of communication that's currently happening between the REI practice and patients right now, maybe more than half so when that happens, Rafi not if because it will happen. It's only a question of time when that happens, what is the RBIs role going to be?





Dr. Ravi Gada  37:12

And you know, I mean, I think people worry about this a lot, right? People talking about not just the role of the RBI, but the workforce is these are these technologies going to replace the workforce. I mean, whether it was the calculator, whether it was Microsoft Word, whether it was, you know, all these different technologies that keep making us better and better. But we talk about this all the time in our field, that there's a under underserved population, there's, you know, we're at the tip of the iceberg. Maybe we're only meeting five 10% of the populations need. Does this actually make us better? Ultimately, we're still proceduralist we still do a lot of procedures in surgical procedures, egg retrievals, embryo transfers, IUI. Guys, so I hope or I think this is not going to replace the average ra i think it's going to make us more efficient. I think it's going to make our nurses more efficient embryologist more efficient. But you're right. How does it allow for us? And we talked about how many are the amount of retrievals that an REI can do in a year. And beyond that point, there. It's it's not beneficial maybe to the patient or the ER, and it depends how many nurse practitioners do you have underneath you? How many nurses? Well, this is going to be another adjunct to that technology have an honestly a checks and balance. I mean, imagine the day where we have going into an IVF cycle. And I'm going to do for the physicians and nurses that listen to the podcast, a Lupron trigger. Well, there's certain things for Lupron triggers that you want to know you want to know that that patient has regular menstrual cycles and that they have a normal FSH level. And so the second you order a Lupron trigger, that the that the AI actually scours the EMR and actually pings you and says, Hey, I don't see an FSH level on this patient. Are you sure you want to order a Lupron sugar? And I say, Oh, I'm glad it caught that. Let me order a FSH level real quick and make sure. So I think it'll make us more efficient. It's, you know, replacing us I think we're all going to be replaced one day, you know, whatever, whatever, you know, sector you're in, you're gonna get replaced 100 years ago, everybody was a farmer, or at least knew somebody was a farmer. Today, I don't really know that everybody can say I have a first degree relative. That's a farmer. So machines have already replaced, farmers machines have replaced manufacturing jobs. And that's the worry about this type of AI technology. It will replace jobs, but it will also create jobs. I mean, we didn't have the jobs we have today that, you know, that didn't exist 100 years ago. In fact, I don't know what the population of the US was 100 years ago. Let's make it 100 million people. Today were 300 million people, no manufacturing jobs, very few farming jobs, and everybody's still employed. So there will be new jobs created. Maybe we'll figure out newer ways to help people get pregnant, but things that are replaceable at Everybody should be looking at saying, you know, how do we either make ourselves better to stay ahead of it? Or how do we use it to, you know, augment what we do today?





40:09

And there's there's a lot of people out there far smarter than us that have kind of pondered upon this question as well. One of the other things that I think is kind of changed recently, is initially they thought a lot of low skilled labor would get replaced fairly quickly by automation and AI and things like that. I think chat GPT tests that a little bit and saying, Hey, listen, well, you know, if your job is sitting behind a desk at a computer, basically, replying the emails and doing things like that, there's a lot of risks there, probably more so than a surgeon, or, you know, even a mechanic at that point in time. So I think that's what it's changed kind of some of the view of what would get replaced by AI first, but I do think we're still a fairly long ways away from that, like, years, at least,





Griffin Jones  40:56

well, for now, and I do want to talk more about that. And we'll definitely end on a note where we're really freaking people out, but, Robbie, I want you to think a little bit about what it is that the REI will be needing to do in these coming years as Chat GPT gets an AI in general gets more sophisticated, like how I'm envisioning it is there's human Gada overseeing a hunt the capacity that robot Gada can do and robot Gada is helping to treat 100 patients and human Gada just needs to oversee robot Gada or is that not the right way of thinking about it? Because the human will soon not be?





41:38

Grip? I think the jury's still out on whether or not Robbie's a robot or not.





Dr. Ravi Gada  41:43

It could be it could be, do you wanna see dr ga da, or Dr GA D Ay ay ay.





Griffin Jones  41:50

Oh, it's already there. And and so what's the relationship supposed to be? Yeah,





Dr. Ravi Gada  41:56

I mean, I think ultimately, that relationship kind of goes back to, you know, we already use or have our staff help us accomplish what we accomplished in the day, I don't accomplish in a day, you know, very much if I don't have a nurse, an embryologist, a medical assistant, a billing person. And this will do the same. I think that, but I do think you know, we've managed to have talked about there, I'd love to do a commercial where I have four consultation rooms running with a iPad in there that's actually has my own avatar, speaking back and forth with that patient, one patient, it's their new patient console, the second room is their return visit with their lab results. And the third patient is coming back for another FET after a successful delivery. And all the while I'm actually over in the operating room doing the retrievals all day. I mean, so that day is coming. Now the question is, is that coming tomorrow? No. Is that coming in the next three to five years? Probably not? Is that something that we can work towards in the horizon of a 10 year type cycle? I think so. I mean, I know that might not sit well with some people. But I think you have to embrace this technology. We are looking at this very heavily. We're investing a fair amount of resources to figure out how to do that. And I think that the people that do will do well, I think the people that resist it may do well. But I think there's a high chance that they're not going to be able to be as efficient if they don't adapt to technology, which is the story over the last 100 years.





Griffin Jones  43:30

You talked a bit about it's some of this like data entry type of work that is most vulnerable. And I was hearing one expert on this topic talk about that it's actually more white collar work that is vulnerable rather than blue collar work because blue collar work tends to be more manual. But Manish when are we going to see an intersection between robotics and this type of AI because once that happens, then we don't need human God at all, once we have a robot that can do the very sensitive maneuvering in surgery that the best surgeons can do right now. And we have the artificial intelligence of all of the data points gathered from every surgery ever electronically recorded. When can we what progress are we seeing towards robotics and artificial intelligence? converging?





44:31

You know, it's actually something that's, that's familiar, before even AI right, it's the separation between engineering and technology or software. Right. And so this is I think, why we're seeing this is because replacing things that are soft like on a computer or something like that becomes a lot easier once you can get over a kind of the intellect or the brain of it, right? The biggest issue with robotics right now is probably the expense and so when In the cost of robotic arms, robotic equipment and stuff like that, that's reliable and high precision and things like that start coming further and further down. That's when you'll see this kind of cannibalize even those types of industries. And so that's where I feel like, you know, this low skilled or blue collar laborers, you said it, you know, as a little bit more protected, because the cost of those robots has not come down. And the functions that they pervert perform, and the accuracy of what they do, just isn't quite as inexpensive as, you know, your email solution of being able to message back and forth with patience or something to that regard. So it's going to happen, but it's just, you know,





Griffin Jones  45:42

so maybe there's a silver lining to all of this supply chain crap that it's slowing down the inevitable





Dr. Ravi Gada  45:49

grip. I don't know. Are you old enough to remember the Jetsons? I mean, that's where Yeah, remember





Griffin Jones  45:53

the Jetsons Flintstones crossover?





Dr. Ravi Gada  45:56

Yeah. So you know, I mean, imagine I mean, the Jetsons is looking forward to, obviously, if robots robots replace what we do, and we work, everybody's concerned on what would we what maybe we start enjoying life again, you know, we worked so hard, we, you know, is a society. And I'm not talking about just fertility, I mean, globally. And maybe we actually, you know, a 40 hour workweek becomes a 20 hour workweek. And we actually are able to read and spend time with family and travel. And maybe I mean, robots taking over and doing certain things. I'm not saying they're taking over the world. But maybe we get back to the point where society actually has time to do the things we do rather than being in this hamster wheel that we are in today.





Griffin Jones  46:38

Before it does, what other applications do you see elsewhere in the fertility industry and quote, so you talked about the applications that can happen in the practice between fertility providers and patients? But where can what other applications are we seeing right now with open AI, if any, in the fertility industry, and what more should we expect?





Dr. Ravi Gada  47:01

Yes, I don't think we're seeing I mean, I haven't seen it, I tried to keep a pretty good pulse on what's going on. I haven't seen it. There's some chat bots that are out there. But overall, in terms of chat, GPT, I don't think so we've seen it in obviously, in the lab, there's a lot of work being done to robotics and, and automation and AI. But what's interesting is, I don't, I think also no one in the fertility space, or even a lot of other spaces are going to actually be able to build their own technology on this, they're going to have to leverage I mean, think about Microsoft, Google, Facebook, Amazon, few other companies, I'm probably leaving out, but they have the best of the best, the brightest or the brightest, and essentially unlimited budgets relative to ours to do this. So a lot of this is going to be creating API Interfaces into their technologies. And using our datasets. I wouldn't be surprised if the EMRs that are out there are looking at this today, right? The electronic medical records, they're fairly technology forward, they are probably looking at their datasets, because they have actionable datasets. You asked me hey, you know, Hey, Ravi How much does DFW fertility associates? What kind of data do you guys have to feed into Chad GPT. And I've looked at you and say, I haven't even I don't have data. Like, I haven't started gathering that. But maybe I should, maybe we should start recording every conversation we have in the office with a patient and with each other, myself and my nurses, myself and the embryologist to feed this dataset, and is one individual, clinic or user or even an MSO going to be able to create enough data, perhaps but but likely not, it's going to require a collective effort amongst the industry. So I don't think we're there in terms of that. I mean, like I said, there's the earlier stuff, I was telling you writing a letter writing a contract for third party reproduction. But in terms of the high level stuff, it's got to be a concerted effort of gathering that data, putting it in, and then really, ultimately, you know, garbage in, garbage out. So if you put garbage data in, you're gonna get garbage data out is what that term is. But you've got to do that, then you've got to test the model over and over and over again, because in healthcare, we demand 99% excellence, right? In other industries, they might say 80% lunch, this, you know, we've all talked to a, a answering machine bot on a customer service line, they'll get to 80% and be satisfied with the quality of that work. We have to exceed that above 99%. So no one's there yet, but the question will be how do we get there? I think that a lot of people like us and others are looking at this. And I think that it's around the corner. If you ask me what does around the corner mean? I can't tell you the answer.





Griffin Jones  49:54

So I was going through Dr. Rudy Giuliani's workflow with her and I How she did 1300 retrievals last year and I was thinking of each of the points, she was talking about listening well, I could impact that I could impact and I told her, I said, You should listen to this episode that I'm going to record with Ravi and Monique, because she was talking about her scribes. And I was just thinking your scribes are gone, man, they're not they're not going to have a job in a couple of years. There's no way in schedulers to right.





Dr. Ravi Gada  50:23

Yeah, yeah, exactly. Or are their job changes, right? You know, they, you know, they either they're either gone, you're correct, or it changes, right. So we still like concierge service, right? So they, the bot kind of does that. I mean, Google right now, I think has a platform that you can order a pizza now through a bot or make reservations at a restaurant. And it'll actually if the restaurant doesn't have something like open table that you can go online and do it will call the restaurant and make the reservations for you and interact with the hostess without, without a person, it's a robot talking to a hostess. So those jobs will be either replaced or used in a different way.





Griffin Jones  51:03

Sometimes those applications come and they circumvent solutions that you would think need to happen, right? So for one of the things that we've been saying for many years is that millennials don't want to talk on the phone. But Gen Z absolutely won't talk on the phone. So you guys have to figure out your scheduling, you got to figure out this digital scheduling as well. Maybe you don't, because this Gen Z person can just input into chat GPT called the fertility clinic and make an appointment for me.





Dr. Ravi Gada  51:34

Yeah, that'd be ironic, as we keep focusing on how can we get the clinic to be the Chatbot. And we find out that the Gen Z is actually or the chat bots, and we're still interacting with them on the human side? Well, unfortunately,





51:45

they're not gonna go to the metaverse to schedule appointment anymore. So





Griffin Jones  51:50

well, it's just kind of one of these principles that you think of that we often it's, we have to build a certain type of infrastructure. And there were many countries, for example, that never really built out a telephonic infrastructure never had landlines at scale. And that was probably in their government central plan that, okay, 10 years from now, we're going to build telephone poles and have the wires out to the rural countryside. And they just never had to do that. And so there can be a number of applications that we're thinking of, for artificial intelligence that just circumvent the need for us to build out some other kind of solution.





Dr. Ravi Gada  52:31

So the other day I took I had an Excel sheet, it was a financial Excel sheet. And I took it and I was just curious, because I had heard people were doing this, I copy and pasted it, I didn't format it. And I thought what happened, so I just copy pasted into chat GPT, it looked awful. And I hit submit. And it summarized the Excel sheet for me without even having cells or columns or anything, it was very oddly formatted. So imagine taking the entire data set that we have for IVF patients and outcomes, and just dumping it into this thing. And just at first go saying, What do you think of this? Or tell us in patients less than with a Hmh? A 42 year old with an AMA H of 1.2? Whose BMI is this? Who has unexplained infertility? What what what what should we do? I don't know if that will be the answer that we're looking for today. But that's what we're probably looking to strive for. And, and that's literally just copy and pasting an Excel sheet. Imagine once you get these API's start working with these companies, and you really integrate with them to provide this type of data. I think it's, I think it's also like people, it freaks people out. But I think that when literally, when the calculator was invented, people thought, no one is going to know how to do math, we're all going to be stupid, nobody is going to use their brain anymore. And they're just going to rely on this device. And here we are today doing way, way more amazing things and advancing technology. And the calculator is a tool that you just use, and honestly half of us have moved away from that to things that are on our computer now.





Griffin Jones  54:15

Okay, so we can spend the next 10 to 15 minutes concluding this topic with going down these rabbit holes, because this is going to be fun, what you just brought up Ravi, the example of the calculator, how it's going to make people dumb, and people aren't going to know how to math do math anymore. Ravi, that did happen for probably 80% of the population. They can't do math anymore. And May and 20% can do math into levels of application that we had never even anticipated before. And probably a square root of that number is, you know, has just magnified the Einsteins of the world. But isn't that number getting smaller and smaller and smaller. smaller and the, the applications are greater and greater and greater. But eventually doesn't that number just become nil, because there's nothing that a human being can do to add value to collective general artificial intelligence,





55:17

definitely the edge of what we're talking about, I think Robbie talks about, like these alternative purposes for humans, and basically, what's going to create our, you know, Will and an ability to keep driving forward and stuff like that. And I do think that that those things will happen. But I do think there's a lot of fear around just that, which is, hey, listen, does the population as a whole get less intelligent? Or does a proportion of the population become less intelligent, and then you have this, you know, small niche of the population that continues down the road of research, and basically innovation and stuff like that. And that, you know, that's entirely the storyline of that time machine movie. So so i think i digress to the point





Dr. Ravi Gada  56:02

where it is, right. I mean, people have, maybe, maybe people have become worse at math worse at spelling, because Microsoft Word and everything auto corrects your spelling. And the older generations, like, gosh, we knew how to do all this, I feel like that sometimes. But the newer generation says, Well, you might know how to do math, and you might know how to spell. But these influencers are able to create a whole new, you know, industry, and they're able to create content, videos, edit it with through a computer that does it all with them. And it would take me eons of time to do that. And they can do that in a matter of an hour. And it would take me days, and I still might not get it right. So I might know what you know, the square root of 256 is and they're like, well, that doesn't matter. I've got a computer to help me do that. But you can't use the computer the way I can. So smartness is dependent on the tools that we have, I think that it, it forces people to be resourceful, and be able to use the tools you have. So just like you use a calculator, just like you use Microsoft Word, you're gonna have to learn how to use AI, and whether it's chatting GPT, or some other platform. And someone else might say, well, I could have written a beaut, I can write a beautiful act or essay on my own. Well, that's great. But if someone else can use a tool to do it 10 times faster and 10 times cheaper, they're probably going to win the race.





57:32

And we've seen this from a software point of view, we've seen this over the last, however, long, 40 years or so, right? Where software is now becoming easier and easier to produce, even what developers can accomplish in just a day versus what we had to do to do you know, back 20 years ago, just to get the same type of thing done has has totally changed. And so there's a rate limiter at some point in time where it's not going to matter that they can do more faster, because there's just not more to do. But we're not there yet, either. So, you know, our developers use chat GBT already today and just in the last few months, right? It helps them solve problems faster, it helps them optimize code that code faster, and a lot of things like that. But we have a long way to go before it replaces any of the developers. So





Dr. Ravi Gada  58:19

by the way, for for like normal people speak that like language model. This thing can code because code is a language so it can actually code software. And people are estimating 10 to 20% of software at at big companies is already being written by platforms like Chen GPT.





Griffin Jones  58:36

I see what you guys are saying human intelligence, resourcefulness, resilience, that's only one category of concern that I have. Let's pause it for a moment that we remain committed to innovation that we use this time, Robbie, like he says the possibility to be free to pursue other creative pursuits to enjoy life. Let's pause it for a moment that we don't actually get worse at anything. There still comes a point right? Where there is nothing that human intelligence and creativity can do to surpass that which a general artificial intelligence can think of let's let's think of ancient hominids, for example. It's some point they were equal at some point, humans parted with chimpanzees, and they parted ways with other previous hominids. But then not we live in a world where there is nothing that a chimpanzee can do to add value in a human being world other than be observed and be a pet. So doesn't that happen at some point? Where Yeah, no,





Dr. Ravi Gada  59:36

I mean, it's a great point. So what's interesting though, remember, AI and regenerative learning is data. Data input. So right now, someone estimated chat, GBT has 190 billion data inputs and it regurgitates it out. But it doesn't know what to put out unless it's been put in. So Chad GPT, for example, is likely or any AI is is likely not to figure out How to create this nuclear fusion between protons to generate energy, human intellect still is able to do that, right? They call it the neural network inside of AI. And what's in there is what's been inputted by humans. So a lot of people are saying that what's inside of the datasets, there'll be able to, you know, AI will be able to find it faster, regurgitate it, remodel it continue to do that. But it's always going to need to use or I say always, I should say, as of today is it needs source data, it needs innovation. So innovation is still going to come from humans. And we're going to do that. And then we submit it into a platform such as AI, and go from there. But as of today, I don't know that anybody has any great use cases of AI solving a problem that humans needed to invent or get to, it's really regurgitating all the things we have. And it's just gathering it faster and spitting it out faster. Maybe one day, we'll be able to have, you know, its own neural network that actually generates new ideas, but new ideas are still created by humans and put into the computer software system.





1:01:12

So I do think that there's some places where we're getting there, right. And that has to do with the sheer sheer compute power, right? This ability for it to go after large, large sets of data, right, and basically go through every permutation, right? So it's a little bit different from what we would think about as like new ideas. It's not necessarily a new idea. It's just a, hey, we've gone through every permutation of possible outcomes. And that's how we get there. And so there's, there's this, you know, looming threat or looming kind of, you know, fear of the fact that hey, listen, there's not anything more that we can do that hasn't been done by AI. But I do think that's right now, it's science fiction, at some point in time, it probably will become reality. But hopefully, it will be past my time.





Griffin Jones  1:02:02

The operative phrase that Dr. Gaga was using was as of today, and I think it's okay, as of today. But even Manish can think of a couple of applications where it's starting. And so what about what how long is as of today lasts for? Is it 10 years? Maybe? Is it 100 years? Probably not? Is it 1000 years? Almost certainly not? Almost? Certainly not?





1:02:26

Yeah, in grip. The interesting thing about that is that it's not a conversation about RBIs at all right? No, it's, you know, it's a





Griffin Jones  1:02:33

human race. Yeah. But it's the relevance of the human race.





1:02:37

Yeah. But even before that just passed, are you guys it's, you know, a cure for fertility, right. It's basically, you know, what's the pursuit? What's the purpose for, you know, humans and its happiness, and, you know, procreation and all these other kinds of facets. And so yeah, we'll get to a cure to fertility probably sooner than unnecessary need for humans.





Griffin Jones  1:03:02

I actually think it's going to be the thing that puts us all out of business, because I think it could even it could happen before a cure for fertility. I've said this for years that my long ball sci fi outcome is that,





1:03:16

but it'll be sustaining, right? It's putting us all out of jobs in order to sustain us otherwise, even the AI has no purpose without humans, but





Dr. Ravi Gada  1:03:25

it puts us out of business for what like we all are doing things so that we can be productive and earn money and then use that and enjoy life and have a purpose. But purpose will be redefined as it just as it was 100 years ago, where it is today. And it will be redefined again and another 100 years.





Griffin Jones  1:03:44

So I actually think it puts us out of the business of production. I mean, the the intersection of artificial intelligence and of virtual reality, I think that's what ultimately puts us out of, of the business of human production. Because when we can live in a world where we can augment our intelligence with artificial intelligence, so human beings are already cyborgs. This these devices that we carry around on us help to us to augment our intelligence and our communication abilities and all of our memory and then once that becomes further integrated with our brains with our nervous systems, and there's a virtual world in which we're able to participate, then eventually, what do you even need to reproduce physically in this physical world for you can have your augmented intelligence baby in your augmented reality world that never has to worry about dying that never has to worry about sickness that doesn't have to worry about human suffering. And I'm not saying this to you guys are smiling. Most people are going to be listening to this episode and not watching it so they can't see you smiling right now. I'm not saying this to be dystopian. I think this is just what's actually going to happen.





Dr. Ravi Gada  1:05:00

about maybe it puts us out of the business of being productive production, but it actually puts us back into the business of relationships and, and, and leisure and lifestyle.





1:05:10

And, and just to just to touch a little bit on the philosophical side of this, right, is just keep in mind the lifespan of a human is part of evolution. So,





Dr. Ravi Gada  1:05:24

that was pretty deep. I don't even know what that means.





Griffin Jones  1:05:26

Yeah, explain that many.





1:05:29

Yeah, so just kind of getting to the point that like, humans live the span of life that they live as a part of how we've evolved to become where we are right now, there's plenty of animals that live many, many years longer than humans and plenty of animals that live much shorter years than humans. And so, you know, that's, that's part of the equation as well. And, and the second thing that's kind of goes into that is it like, listen, we might have purpose with AI, but AI has no purpose without humans, either. Because what does a bunch of bots running around, servicing themselves and doing things for themselves me, either, that's a, that's a purposeless kind of function in that vein as well,





Griffin Jones  1:06:13

maybe, but I'm not convinced of that, they may find a purpose because the purpose of any living organism is just to continue existing. And human beings might be the first one to evolve itself out of existence. You talked about our relationship to other species in terms of how long we've been aren't, we haven't been around very long. It's been 200,000 years, I think, since humans separated from the last hominids. And when you look at our, our growth, it's been it's, it's a hockey stick, compared to the first years of leaving the canopy. And now civilization just in the past couple 1000 years, industrialization 200 years ago. And so I don't think this stuff is too far away. And I'm not trying to be dystopian, I just, I just don't think that I don't think there's any way for us to be able to contain it and control it. And so far you guys ever given otherwise?





Dr. Ravi Gada  1:07:09

You know, I think that people thought that when assembly lines came about, I think that they thought that when tractors came along, I think that is always been a worry. And it will always continue to be a worry. But ultimately, in a philosophical sense, humans are resilient. And like I said, we seem to stay ahead of the technology that we create ourselves. You know, at what point do we are we not able to stay ahead of it? Well, up until today, we still have I mean, people thought the world was over when assembly lines came in, and manufacturing jobs just got crushed, and what are we going to do and farming got replaced by equipment. And here we are today, three times the population with you know, 2% 3% unemployment, I mean, people are still employed doing something?





Griffin Jones  1:07:56

Well, if they said that, in the 1860s, as folks, were moving from steam to coal, you know, the late 1860s, or somewhere before the early 1880s. Whenever that happened, if they said, This is the end of humanity in the in the next five years, yeah, they would have been wrong. I think it's the amount of time where people get things wrong. I don't know if this is going to happen in a century or in a, or in an eon or a millennia. But I think it's inevitable that it will,





1:08:31

from that point of view, right? There's a this is not a country point, right? This is, you know, a we're never going to know, or we're not going to know, anytime soon. But in addition to that, yeah, I mean, it's definitely a possibility. And we'll have to figure out something else to do or something else to be or some other purpose to have, at that point in time. But, you know, it's, it's a tricky question, and probably well beyond our scope. So





Griffin Jones  1:08:59

it makes the premise of matrix a lot more interesting, doesn't it? You will never know except, and then and then what will happen? Well, if if you could, if you could evolve yourself out of existence, and then the only thing you had left to do was to recreate a previous existence? What period would you go back to accept the end of the 20th century? And it makes the promise even better,





Dr. Ravi Gada  1:09:22

right, right. Now, I've thought about the matrix A lot, you know, in looking and hearing about AI and its evolution, and it really makes that movie a lot more relevant.





1:09:31

Yeah. My only claim is I don't think they'll need us for batteries. So.





Griffin Jones  1:09:35

So you guys are optimistic. And I know that I might sound pessimistic, I don't think I'm being passed out. And I'm not making a value judgment. If all of this thing is is good, bad or neutral, but I want you guys to think a little bit about second and third order consequences. So Did either of you watch any of the interviews that Brett Weinstein has done about chat? GPT I bet but most of my audience doesn't know who Brett Weinstein is though. Those of you that do, I bet it's half and half about half the like, really critical thinkers really like him. And then other people might not like him because he's like the guy in the movie that is worried about everything. And he's always trying to warn about the media coming. And he's, he's, you know, he's worried about civil war. He's, he's very worried about the entire scientific and medical apparatus and feels that vaccines were rushed in that, you know, that that system was compromised, even if the vaccines themselves are safe, he feels that the the system was co opted. And one of the things that he's worried about is chat GPT given our fragile social relations right now and human beings, general incompetence to assess expertise already, you know, your peers, Ravi are very What are your peers often complained about is Dr. Google? And so if Dr. Google is them, though, and it's a avatar of them pulling from collective data points and, and its expertise that may or may not be scientifically grounded, then what are some second and third world? I'm sorry, second, or third order consequences that you might be concerned about?





Dr. Ravi Gada  1:11:15

Here? I mean, Brett Weinstein, he goes into things like it's able to pass exams, it's able to actually change GBT our licensing exam, as physicians is called the USMLE. It has passed both of those exams. And so if it's able to pass those exams, and people can access it on the internet very quickly, how do we discern who really knows? And who's just using chat GPT to present the answer? And I mean, there's two facets, I think, to that. dilemma. One is, you know, we all have been in oral exams, we've all taken exams in classrooms. I mean, the tool is only as good as you can access a computer and internet and be able to ask it those questions. But there's still a way to assess in education, because his big issue is education, and how people are using it to write essays and pass tests and do these things. Well, we've moved to a virtual education model post COVID. And maybe this brings us back into the universities, doing oral exams. I mean, you know, we've all been there. And and, and you can assess that in real time, you can assess an essay when you have Chad GPT able to write an essay for you, and how do you discern who's a good student and who's not. But again, in person education, we'll do some of that. The second part is, we already have things like chat GPT. Today, as physicians, we have up to date that we use as a resource. I have my partners, I have my colleagues, if I have a case that I'm not sure about, I pick up the phone, I talked to somebody, I get some information. I mean, it's a resource to augment and help our ability, but I think he does a lot of fear mongering, I think he likes to just the world is ending and everything. And that's okay, itself. But ultimately, there are ways in the education system to figure out who knows the right answer, and who doesn't, without having them taste, take tests at home. In the real world. You know, he gave an example, I think, at one point, have an auto mechanic and you just go in the auto mechanic asked Chad GPT. And he just sounds really smart. But how do you really know he knows, versus an auto mechanic who's been around for 20 years? And at





1:13:26

what point in time? Does that matter? Right? If I can get to the right answer, either way, right? It doesn't matter if the auto mechanic use chat UVT or not.





Dr. Ravi Gada  1:13:34

I mean, sometimes when I see someone come to the house for work, or you know, we're interviewing someone, one person might be really old school and has 20 years of historical knowledge. And the other one's a whippersnapper who uses all the resources around them to get to the answer. Which one do you want? I don't know. But that, you know, that depends on you know, what you're looking for?





Griffin Jones  1:13:53

Well, you talked about the assembly line, the farmers, you know, how those jobs have gone away, and how a lot of wealth was created by better jobs. And it really depended you. You all live in Texas, where you have a low regulation, low tech state that saw a lot of growth, but I live in a part of the country where many cities were decimated because they didn't adapt. And so you see different types of trajectories, I guess we would have to have a whole other conversation beyond our pay grade of what is the equitable distribution of, of benefits after chat GPT How do you even materially divide the spoils? And is that something that's possible to so that everybody can enjoy life as opposed to some of the people being able to enjoy life more from chat? GPT Are either of you guys? truckies





1:14:47

when I was a kid, I watched the soundtrack all the time. Yeah, the original





Griffin Jones  1:14:50

are next generation, next generation eyes. So next generation all the way what I'm hoping for is the holodeck. If we can all get the holodeck out of this you Then I think that's where the where the trade off. This has been the closest to any kind of Rogan episode I've ever done with you this is we're recording at almost 1130 at night on the East Coast. And I really could talk to you guys for three and a half hours about this. But we'll save that for another time because people are gonna listen to this, they're gonna Monday morning quarterback me just like Dr. Gowda doesn't say you should have asked them this you should have. And so I'll compile that I'll and I'll happily have you guys back on for a second time because this has been a blast. We've talked about the applications for the REI practice and for fertility patients. But we've also talked about the potential implications for the human race because you can't possibly contain this topic to just the REI practice, even when you're focusing on the applications for our field. It just goes so far beyond that. So how would you both like to conclude?





Dr. Ravi Gada  1:15:57

No, I mean, thanks for having us. Griff. You know, I know we've talked about coming on this before. But this was finally a topic that I feel very passionate about. I think that healthcare in general should embrace this. And I think that health care at a high level, will we as people in the side, the fertility industry have to figure out how do we take the data that we have, and not just data inside of the EMR, but all kinds of data to make sure we keep up and so we are working on this, you know, continuously, I think that others will join in and it will make us better, it will make our patients better, it will make outcomes better. So I'm not worried about the technology of the consequences of what does it do to jobs or do to us, but more how much it's going to improve our efficiencies and our outcomes. So those are the things that I think that technology helps. And technology is deflationary by nature. And maybe this also helps bring down the cost of IVF, which could help us be able to access more of the patients that are out there seeking care. So that's how I would, I would leave it.





1:17:04

And just that on the roof. Absolutely. This is a fun topic. You know, it's one of the ones that I think, you know, I can talk about tech all day long. This is one that, you know, definitely over the last few months has definitely been top of mine. Something that's just interesting has so many implications in fertility as well as far beyond, you know, any of your users that listen to this, if they haven't had a chance to even just log in, and just play around with. I mean, it's a different feeling right? To read an article about it versus actually start asking your questions and see what you'll understand a little bit why we're so excited about it. But appreciate you bringing us on the show. This is a lot of fun,





Griffin Jones  1:17:45

Manish Chaddua,  Dr. Ravi, Gada thank you both so much for coming on the inside reproductive health podcast. I look forward to having you back already.





Sponsor  1:17:54

This episode is brought to you by Univfy, email Dr. Yao at mylene.yao@univfy.com or just click on the button in this podcast, email, or web page for your free IVF artificial intelligence tips and strategies.  

Today's advertiser helped make the production and delivery of this episode possible for free to you. But the themes expressed by the guests do not necessarily reflect the views of Inside Reproductive Health, nor of the advertiser, the advertiser does not have editorial control over the content of this episode. And the guest's appearance is not an endorsement of the advertiser.

You've been listening to the inside reproductive health podcast with Griffin Jones. If you are ready to take action to make sure that your practice thrives beyond the revolutionary changes that are happening in our field and in society. Visit fertility bridge.com To begin the first piece of the fertility marketing system, the goal and competitive diagnostic. Thank you for listening to inside reproductive health

103 - Supply vs Demand and Artificial Intelligence in the Fertility Field with Dr. Robert Stillman

Understanding the past can often help create clarity for the future. Many industries are changing rapidly these days and Fertility practices are not immune. Changes from scientific advancements, culture, and consumers all play a role in the landscape shift of the industry. When you add technology to the mix, advancements start snowballing rapidly.

This week on Inside Reproductive Health I interviewed Dr. Robert Stillman, a Board Certified Reproductive Endocrinology and Fertility subspecialist with over 40 years of experience. We recount his experience from beginning to the present and what he deems will be important in the future. He has direct experience with the integration of private equity capital into fertility practice and has led trends in practice financing, technology (e.g. AI, genetic testing, egg freezing), physician and staff recruitment, retainment, compensation, partnership tract, and retirement paradigms.

In this episode, we talk about Dr. Stillman’s insight into the industry and big trends we are seeing including how Artificial Intelligence is and will continue to shift the industry. We also talk about:

  • How Private Equity effects Fertility Practice

  • What changes have happened in the Fertility field over the last 20 years

  • How has consolidation and expansion has affected the REI landscape

  • How Bob was able to successfully work with the academic centers


To learn more about our Goal and Competitive Diagnostic, visit us at FertilityBridge.com.

82 - The Business Case for Fertility Surgery, an interview with Dr. Matt Retzloff

On this episode of Inside Reproductive Health, Griffin talks to Dr. Matt Retzloff, a Reproductive Endocrinologist from the independently-owned Fertility Center of San Antonio. Dr. Retzloff is board certified in both RE and OB/GYN and has special interest in fertility-related surgery, focusing on minimally invasive surgeries.

Dr. Retzloff is a firm believer that surgery for infertility-related issues are best managed within a fertility practice, allowing for continuity, confidence, and best outcomes for the patient. But looking at it through the lens of business, those benefits don’t always align with business operations and finances.

Together, we dig into the pros and cons of keeping fertility surgery in the purview of the REI.