How H1 Is Networking the Healthcare World, with Ariel Katz
Katz co-founded H1 in 2017 and he’s still the CEO today.
And what he’s getting at, with that description, is the fact that the world of healthcare is incredibly fragmented.
Before H1, there was no central database or platform that everyone can use to share their professional profiles and get in touch with colleagues.
Physicians never adopted LinkedIn for this kind of networking because they just don’t switch jobs very often.
And because doctors are spread across so many different institutions, there’s no central directory where you can just enter a query like, “Show me all the oncologists in Omaha.”
That’s frustrating not just for patients but for doctors themselves, especially those involved in biomedical research, because it makes it hard to find collaborators.
And it’s an even bigger frustration for drug companies, who need to know which doctors can help them enroll the right patients for clinical trials.
H1 is trying to solve all of those problems. And to do that, it’s built what Katz says is probably the world’s largest graph database of people in healthcare.
Just three years ago the company was still part of the Silicon Valley startup incubator Y Combinator, and it’s now raised almost $200 million in venture capital.
I wanted to bring Ariel onto the show to talk about how H1 has grown so quickly, and how better networking could accelerate drug development and help patients find the best doctors for them.
Here’s our conversation.
Harry Glorikian: Ariel, welcome to the show.
Ariel Katz: I’m excited to be here. Thanks for having me.
Harry Glorikian: Oh, yeah. It’s great to have you on. I mean, I’ve been trying to wrap my head around all this stuff you guys are doing at H1, but I want to step back a little bit for everybody that’s listening and. You know, just ask you to sort of explain the origin story of H1.
Maybe you could start by talking first about how you got interested in the problem of this world of science and health care and sort of bringing this data together.
And I if I understand correctly, you started, back when you were in college a service not totally dissimilar called ResearchConnection. And start back then and bring us to here and sort of what motivated you to start one and all the rest of that.
Ariel Katz: Yeah, happy to. And it’s a good question. So I started ResearchConnection way back in the day with the idea, the original idea was help get students more involved in research. So if you if I ask you a simple question, tell me all the latest biotech research going on at Harvard right now, it would take a while.
Or if I told you, tell me all the new mathematics research going on at Harvard, you literally couldn’t tell me that. And so we said, that’s crazy. These are incredibly important institutions. And you can’t find out all the research that’s going on there. So as a student of Harvard, it’s hard for you.
You’re trying to do research at Harvard. It’s hard for you. And if you want to get into a PhD program and you’re trying to find who’s doing the top research around the country, it’s almost impossible. And you have to click on different websites, read different publications, everything around it.
And so that was the initial problem we set out to solve with Research Connection, help get students more involved with research and allow a place for researchers to showcase their research activity. That was cool in that it worked. What we quickly learned is researchers want to work with students and they have funding.
Ariel Katz: And so when they have a grant or they have a grant from industry or government or nonprofits or whatever. And so we quickly had to learn about how does research get funded. And the trends were obvious.
Everyone felt like it took so much time to get funded grants from the NIH, funds about $40 Billion a year in research was like flat or slightly declining. Not for profit wasn’t growing that much.
And industry, pharma, biotech, large corporates was where it needed to be filled. And so we ended up growing ResearchConnection. We sold it and then myself and a couple of the guys from the ResearchConnection team were like, What do we work on next? And some of them wanted to do like a website for athletes, a social media app.
And I was like, I don’t know much about anything in this world, but I think I’m in the 1% of knowledge around profiling, of research activity and understanding how research gets funded.
I think I know more than most people in the world about this. So I think if we do this, we would be successful, and that’s how it all started. And then I pitched my current co-founder on the idea and he was into it.
Harry Glorikian: Now, no retirement, huh? But now you guys, if I’m not mistaken, you guys were also part of Y Combinator right back in 2019.
Ariel Katz: 2020. So we were the COVID batch, COVID hit, so we were in YC in 2020. YC was a lot of fun.
Harry Glorikian: So. So what did you get out of that experience? I mean, you guys were just getting started. YC I mean, they’re all over the place. They’re doing bazillions of things, so.
Ariel Katz: Do you want to hear the story about YC, it’s a good story.
Harry Glorikian: Sure, why not?
Ariel Katz: So the VC firm that, they’ll know who I’m talking about, I won’t say their name. So we had we had a few million bucks in revenue at the time before we joined YC, and we had signed a term sheet with the VC fund and we were just going over deal docs. They were taking a long time and they went too long, which is their mistake.
And I had, I was moderating a panel with one of the partners at YC, like I was the moderator and there’s three people on the panel and there was the partner. After that, he came up to me and said, we’re not doing a good enough job reaching New York companies. You should be in YC. I said, No, you’re crazy. We’re later stage. We’re about to sign this series A. And then he emailed me and then we hopped on a call and he said, You’ll get better terms if you come to YC. And I was like, No, we’re here. We’re later stage and it didn’t make sense.
They said, Come out to San Francisco, fill out the application. It’ll be worth your while. And that’s how we ended up getting into YC. And so we it was funny, we filled in the application late. We went out there early. It was a whole thing because we had a certain deadline until the actual deal time with the term sheet that we had. And so we went through this whole maze, but then we decided to go into YC in the end.
Harry Glorikian: Well, if the $13 Million Menlo Ventures deal had, they were right.
Ariel Katz: They were right. Our term sheet, the one before was at a $18 Million post-money valuation. Menlo was more than that. And so they were right.
Harry Glorikian: So you guys have raised…
Ariel Katz: $193 million.
Harry Glorikian: Yeah, I was trying to do the math there because I’m like, I’ve got all the numbers here in front of me, and I’m like, Wait, wait, wait, wait. It was $58 million in 2020 and then $123 million in 2021 and early 2022. That’s fast growth. I mean, I guess it begs the question of why do you need that much money and what are you doing with it?
Ariel Katz: Yeah, you never think you need that much money. So Menlo’s been a great partner. So when Menlo invested like five months later, they were like, You’re going to raise your series B very soon. It’s like, What are you talking about? We have all this money in the bank? You just put the little money and they’re like, No, no, no, you don’t get it.
You’re going to want this. I said, Okay, well, are we going to raise like $20 million? He’s like, That’s way too little. You don’t get it. Ended up raising almost $60. But we’re originally thinking we would raise $20 million, which I thought was a lot coming up from $13 million.
The way that they described it, which is true, the environment is different now than it was then, but at that point in time, they said with each round you have cards on the table. The more money you have, the more cards you can flip over. Not all the cards will work, but sometimes they will.
And you want to have enough money to do enough experiments to be able to hit what you need to get the milestone for the next round of funding. And so we flipped over a couple of cards between our Series A and Series B, and they worked, and flipped over more cards between our Series B and C.
And so these are expansion of product lines, expansions geographically. Continuing to invest in what our users want. And so a lot of money is put into engineering product and data.
Harry Glorikian: So. If I’ve read correctly, like you describe H1 as sort of a LinkedIn for health care. And so here’s a dumb question, but why isn’t LinkedIn the LinkedIn for health care? I mean, what additional data or features are needed to make the tool valuable for your customers?
Ariel Katz: Linkedin meets ZoomInfo meets ZocDoc for doctors, is a more accurate description, I would say. There are two segments that don’t use LinkedIn. Doctors, health care professionals and people in entertainment. Why? They don’t need a network online. That’s not the way that they operate.
Doctors don’t move jobs that often. You generally are like a specialist and you’re, a pediatrician doesn’t become a medical oncologist. A pediatrician is a pediatrician. People like us know we can do it. Everyone, I got to be in sales, marketing, whatever. And recruiters are reaching out recruiters. It’s a whole different dynamic.
And entertainment is also incredibly different. Entertainment and athletics very different. Whereas like engineering or marketing or sales or PR are they’re very they’re very different. They operate very differently. Some of those functions. And so doctors don’t use LinkedIn as a data point really that often.
So LinkedIn, LinkedIn’s use cases don’t really make sense so much there. What we do is different though. We have a doctor network where doctors can come in and claim their own profile, but we also have everything you’d ever want to know about them and we collect some of that ourselves.
A doctor doesn’t input it. And then that’s used today by patients, by pharma companies, by insurance companies, by hospitals, by health systems. Everyone in the health care ecosystem uses H1.
Harry Glorikian: Interesting. Yeah. I mean, I was looking at it and. You know, it looked like there was a lot of medical affairs, medical science liaisons, you know, just cutting through a lot of this stuff. Can you help listeners understand a little bit more about. Who the product is really aimed at and. What what is the role your users play inside their organizations and why do they need your help? I mean, sort of.
Ariel Katz: I’ll walk you through it.
Harry Glorikian: If you were doing the pitch.
Ariel Katz: So H1’s mission is to connect the world to the right doctors. We have everything you want to know about every doctor in the world. And so you’d ask yourself, okay, who could use that? Who is it helpful for? It starts with like, is this the right doctor for me to go see, or is this the right doctor for me to work with? Who works with doctors? Pharma companies, pharma companies, med device companies, biotechs. How do they work with them?
One, they work with doctors on running clinical trials. So they use a they use H1 to find the right doctor to run their clinical trial. It’s a very important and big question for them. Generally, they have hundreds of doctors running clinical trials all across the world and finding the right doctor that has diverse patients that knows how to run a successful clinical trials are pretty hard question. So they use us to find the right doctor to run their clinical trials.
And then they also need to educate doctors about their clinical trials. And they have a team called the Medical Affairs team that does that. And so they use H1 to find out which doctors should they educate, which doctors don’t know about a certain therapy, who are the doctors that can change medical practice.
So they use us for that. And then they eventually need to market to doctors. Drug’s approved. It works. Who do I go to market my drug to? And so really across the spectrum of which doctor work with is how they how they use H1 from medical device to pharma to biotech.
Ariel Katz: On the insurance side of the house, it’s different. Insurance companies need to answer one or two questions. One is, which doctor should I insure. Are they a good doctor or not? Insurance companies make money when people are healthy, not on people that are sick. So I want to find doctors that actually are good at their job.
And then the second question is, as a me and you, we use our insurance website to find doctors to go see. And so how do I showcase the relevant information to my members, like people that use insurance website to find the right doctor to go see? And that’s where we’re used as well by insurance companies and digital health companies to for those two areas. And then doctors use our product to learn about the latest science and medicine.
So they come in, they read the latest scientific publications, and then they comment on it, small subsets, a lot of comment on it. And then people read about that commentary and they can also claim their profile to update it so that it updates to all the other people using each one. So it’s really everything you want to know about doctors and that those are a lot of our users.
Harry Glorikian: So it’s interesting, right? I mean, I’ve been in this industry for forever, right? So a lot of the stuff you’re talking about, I feel like, oh, I would go there and then I would go there and then I would go to different places to put these pieces together. Right? So. You know what makes H1 special. Different. Unique. That would change the the normal workflow of how I would do this through other methods.
Ariel Katz: It’s a good question. So it depends on the segment. We’ll start with pharma. So if you’re trying to find the right doctor to run a clinical trial, what’s what they used to might have done is go to clinicaltrials.gov that might look on Google some stuff, they might look at press releases, their internal information, and that’s how they used to do it.
What that doesn’t tell you what one can tell you is what’s the race, ethnicity, income level of their patients. If we’re trying to recruit diverse patients, how do they actually perform on that last trial? Did they recruit patients or not? You can’t get that publicly, so you have to come to each one to answer those two questions. So it really transformed the way that they were doing it on the medical affairs side. You can probably Google right now, best oncologist in Boston. Google best oncologist in Chennai. It’s a lot harder to Google that or Mexico City or in Costa Rica. Montreal might be easy. Saskatchewan. Canada. Who’s the best? Who’s the best pharmacist that understands HIV? Very hard questions globally. And so we really change the game by having a global product that profiles every doctor in the world, about 10 million doctors in there. So it really helped with medical affairs in that sense.
Harry Glorikian: So that’s because if I Google the best, I’m like, okay, this is so fraught with like bias and other things. How do you guys I mean, well, I guess where does the data come from? Where do you find it? I mean, do you. You know, the natural question is, do you get it off websites and take it from there? Do you get it from your own proprietary source? And then how do you know the data is accurate?
Ariel Katz: It’s a good question. So we get it from buckets and themes of areas. One is public sources, like you could go to Mt. Sinai, New York, and find all the doctors that work in Mt. Sinai. But all you’re really going to get is their first name, last name, phone number, specialty.
There are psychiatrists in New York and the Upper East Side. Okay, great. That’s not enough. You want to know the types of patients that they see and you can get that information. You get that from the government in the US and from insurance companies. So we purchase a lot of data as well. And then you want to know, well, what does the person think about the latest science of medicine?
Well, they come into each one and they input that. So it’s like there’s a community element to it, as well as purchasing and partnership, partnerships, as well as pulling from public sources. We want to know what they’ve published on. You get that from PubMed. One of the clinical trials they worked on and you can get that from clinicaltrials.gov.
We want to know if they’re tweeting, get that from Twitter. You know, if they have a LinkedIn, you get that from LinkedIn, you get a whole range of different sources. So for one given doctor, hundreds of different sources coming in for that one doctor, and we squish it all together.
In addition to that doctor coming in and updating their information, in addition to the community, providing information, updating that industry, purchasing information. So it’s all of that coming together about one doctor.
Harry Glorikian: Interesting. And so your. There is a reliance on the doctor themselves or whatever, helping make sure that the information is accurate.
Ariel Katz: Doctors have, they are not the best at writing stuff sometimes. And so there is an element of it. But we don’t always trust what the doctor inputs. Sometimes people want to make themselves seem bigger or smaller than they really are. Or maybe they’re humble, maybe they’re arrogant. Who knows what? And so we still validate whatever comes in.
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Harry Glorikian: So how is the data organized sort of under the hood? I mean, is it a giant graph database like you might be able to find at companies like Google or Facebook? Do you organize it in a way that helps users identify the right connections or maybe if they’re trying to sell to them the right prospects?
I mean, how have you thought about because you’re talking about a lot of different pieces of data coming together to answer very specific questions. Depending on the user.
Ariel Katz: We are probably going to have the largest health care graph out there. A lot of our data is integrated right now in the graph. Not all of it. We’re trying to get more of it into the graph, but it’s really important. You want to know like, is this doctor work with this doctor? Does this doctor refer patients to this doctor?
Do these doctors go to medical school together? Do these doctors sit on the same medical society board, you know, all these different relationships? This doctor is related to this hospital, which is part of this IDN, which is insured by these five payers, and they have a partnership with this pharma company and they’re all relatable entities.
A lot of it is sitting in a graph, a lot of it’s sitting in a relational database. We’re pushing to put everything into the graph and we think we will have the largest graph of health care information or doctors that exists out there.
Harry Glorikian: So I guess that now comes to, okay, you’ve got this, People are using it. You know, what’s the business model? Is it a subscription fee for access to HCP Universe and other tools? Are the fees scaled through the number of users at each company?
Ariel Katz: It’s always free for patients and doctors, free for doctors, free for patients. That’s good for the world, good for health care. And then it’s an annual license. It’s a subscription that pharma companies, biotechs, med device medtech and insurance companies and hospitals pay for. So they pay for it.
Harry Glorikian: Okay. And. How do you know? Whether you’re succeeding or not, I read. A statistic like seven of the ten top ten global pharma companies are customers of H1. But how do you know about the rate of adoption that you’re seeing and. You know, how how is the company doing overall?
Ariel Katz: Success is a funny word. You can answer that financially. You can answer that spiritually. Can answer that in very different ways. So financially, we’re doing well. But that’s a very shallow way to look at an organization. What we look at is more like each of our products have a mission and how well are we achieving our mission.
So for example, our Landscape products mission, by the end of 2024, companies that use Landscape have 50% more diverse patient populations enrolled in their clinical trial than the ones that don’t. Pfizer runs a clinical trial. BMS runs a clinical trial. BMS uses Trial Landscape.
They have 50% more diverse patients. That’s success in my eyes. They both pay us money or whatever. That’s great. But if we achieve our mission, the finances will follow after. And so we look at the fund and the financing is important, obviously, but we want the missions to be achieved first. With precise, we want 100 million patients to use H1 to find the right doctor to go see by the end of 2024. If we do that, I promise we’ll make a ton of money.
But I want to achieve that mission and make sure that we make 100 million people’s lives better and they feel healthier and they feel more informed when they find the right doctor to go see. So we look at our missions. Some of the products are driving really strongly towards the missions. Some of them are experiencing bumps along the road. But that’s how I think about success.
Harry Glorikian: Yeah, every product, it’s impossible for every product to be successful. But now one of the. It seems that the show is always like data, data analytics and you guys have been making, at least from what I was reading, a big push to use more AI to find relationships in your health care provider data that might be hard to find otherwise.
Can you talk about this? Without going into the secret sauce, But maybe what were the limitations of the HPC Universe platform before you started applying more AI tools? How does AI change the platform’s capabilities? And then what are maybe an example or two of things users can find now that they couldn’t do before? That’s a lot.
Ariel Katz: But let’s, let’s, let’s make it real. Let’s go through an example. Let’s say you’re the CEO of AstraZeneca and you’re launching a drug in HER2-triple negative breast cancer. In the US, there are 15,000 medical oncologists that treat patients with breast cancer. Which ones should you talk to? That is a hard question.
Harry Glorikian: Just that’s a much smaller subset, so.
Ariel Katz: Wait, it gets even harder. You only have 50 medical science liaisons, so you have 50 people covering 10,000, 15,000 people to talk to. Which ones should you talk to? Each one can now help you segment that we can tell you. Who are the digital opinion leaders? The top publishers.
The top clinical trials is the people that see the most patients. They are treatment leaders understand how to treat patients. It really carved the universe of who you want to talk to should really be carved out. Okay, great. You’ve identified 1000 people that you want. There are 50 medical liasons to talk. How should you talk to them? What do they care about? What those doctors care about. All right, interesting. What activities, which conferences are they going to?
How do you get in contact with them? Who are they connected to? How could we suggest different ways for you to engage with these health care professionals with your 50 medical science liason team? And so we’re able to personalize this information for them. Instead of giving them an Excel spreadsheet of here’s 15,000 oncologists, not so helpful, it’s now personalized to every user. We do predictions for every user. Here’s the best ways you should be engaging with them.
Here’s a new activity. Here’s a new tweet. This is interesting. This doctor would be interested in learning about this. So your personalization, you have predictions and you have insights. Here are the top 1000, your contact. And but here are the new fellows that just graduated. And here are the five that we think are going to be the new thought leaders in oncology and breast cancer.
You should probably be engaging with them. So insights that you can’t just get by looking at an Excel spreadsheet or something that you could Google. And so those are the ways that it was not possible before. But you start to put in some of these technologies, it creates a whole different experience.
Harry Glorikian: Hell, I want that from my own network. What are you talking about? I’ve got I’ve got a lot of people that I’m connected to trying to keep track of everybody so that that would be useful for me. Too bad. Too bad it’s not available. Like someone could upload their own network and do the same thing.
Ariel Katz: There’s actually a ton of information about doctors, which is why this becomes possible. I think it’s harder when it’s in different industries.
Harry Glorikian: So a few months ago, like I was talking to Lokavant’s CEO Rohit, who is an old friend of mine on the show about clinical trial management, and you guys recently announced a partnership with them where you’re using their data to enhance your Trial Landscape product. So can you explain more about how H1 is being is used by customers planning clinical trials, and what were the reasons for this partnership? What value does Lokavant’s data add to your product?
Ariel Katz: Yeah, it’s a good question. So let’s say you worked at like Biogen. Pick a pharma company, and you’re trying to run a clinical trial in heart failure. How do you do that?
You’re literally going to go to clinicaltrials.gov and type in heart failure. I’m going to show all the doctors that worked on heart failure and all around the world. You have absolutely no way of knowing if they were good or bad. So take a look. You’re going to look at Novartis clinical trial and heart failure and you’re going to see they have 50 doctors that worked on it.
You’re not going to know which ones actually recruited patients, which ones violated protocols, which ones did well or did poorly, which ones completely knocked it out of the park. None of that information is available to you.
Each one has started building a network of this type of information of how well a doctor performed on a clinical trial and event is one of our partners.
To supply some more that information to H1, it is more critical to industry. It’s like the backbone of, bow, instead of having to search on clinicaltrials.gov, I could go and give you access to H1, which would tell you every single doctor that’s ever worked in a heart failure, a clinical trial, and who succeeded and who failed and how they succeeded and how they failed.
Probably want to use that instead of clinicaltrials.gov. And so Lokavant is one of our partners because they have some of this information, for about about 2,000 clinical trials where they have information for. And so we partnered with them to showcase that that in H1. And we do this with other pharma companies and CRO’s well.
They’re one of our key partners in doing this. So it’s a good partnership with Rohit at Lokavant.
Harry Glorikian: Yeah, I’ve been I’ve been on the other side where, you know the company has picked a site and it just blew it. And it’s devastating to a startup company trying to move a product along. If something like that happens.
So if this makes that easier, then I should be telling all of our all of our startups that they should be using this to mitigate that that issue. But talking about clinical trials, you’ve been talking a lot about how. Customers use this data for diversity. And you were mentioning that as one of the goals of the company earlier.
Can you, I guess, talk about the specific problems that are needed solving in this realm and. You know, how is it that your data. You know, makes that difference. Drives that change.
Ariel Katz: Yeah, it’s a good question. So historically, over the past 20 years, having a diverse set of patients. And gender and age and race and ethnicity and income level and communities was a nice to have not a must have for pharma companies that were running clinical trials.
I would say since George Floyd happened, the murder of George Floyd couple years ago now, that’s changed. Since then the FDA has released guidance saying they want to see a diversity plan with every single clinical trial that gets submitted to them. It’s a really key milestone that the FDA did.
We think and I think a lot of people think that’s going to become a requirement and not just guidance anymore. So it became a nice to have to say we’re spending $100 million on this clinical trial. We need one like we need a diversity plan. And so this is really hard, though. It’s very hard for a pharma company to recruit one patient, let alone a diverse patient, into their clinical trial, and they had no information to do so.
What we have is the race that ethnicity, the income level and the educational level of 98% of Americans and mapped onto a physician. So you could see for a given physician, give a doctor what’s the race of the patients they see, what’s the gender, what’s the income, what’s their background, what community they from everything you want to know.
So when you are looking to recruit physicians and doctors and patients into a clinical trial, that you’re actually hitting a diverse population.
This is critical. Covid, different communities didn’t trust the vaccine. There’s not trust in our health care system. If we start to engage different communities, low middle income communities in America and globally, that will build more trust into the health care system and make sure that drugs are created for medicines created for all, and not just a subset of us.
And so we think we like the mission of it and we think it’s important and we will. And we see it happening now and we think it really started like two years ago.
Harry Glorikian: It must be. I was just thinking like, man, I would probably have fun just poking around in there just to see how how the data looks. Right. I mean, visually, just across the US, I’m sure that just looking at it, you’d see patterns that would emerge from that.
Ariel Katz: It is. It is really cool to look at it. It’s not super unexpected. Certain doctors in certain communities see certain types of patients because that’s where people live. So it’s pretty mapped, pretty identical to like how census data looks in the US. And we do this globally, but in the US it looks pretty similar to census data or how you would think or different people live.
Harry Glorikian: Yeah, but it’s, it’s funny, right? I mean, sometimes people think certain things or have a hypothesis and then there’s like you show them really what’s going on.
Oh yeah, it actually does look like this and or we can dig into it. It adds some insight to it. So. You know, it was great having you on the show. I you know, I don’t hopefully I didn’t leave any questions out that you would want to get out there. So is there anything that I didn’t ask that. You want to throw in there?
Ariel Katz: No, this was really good. I really appreciate the questions you asked, they’re insightful. Hopefully, I provide some clarity about what we do. This is funny. This is like a problem that people have solved and they feel like the solution is good enough.
But we know it’s not good enough at all for anybody that you could solve in different ways. Like in 2 to 4 years, everybody is going to go, “How did I live without H1 and know who is the right doctor for me to work with and go see.” We were crazy beforehand. There’s like, if I’m trying to drive now from New York to Boston, I’m not going to use a piece of paper as my map.
You’d be crazy. I want to know the traffic patterns. I want to know where there’s an accident. I have to use Google Maps or Waze. I think it’s going to be a similar type of experience in 2 to 4 years.
Harry Glorikian: I mean, if I would if I could have my wish. Right. And maybe you guys do have this and I just don’t know it, is like the proficiency data of the docs.
Ariel Katz: Quality scores. We’re working on that now. How good is this doctor doing a hip replacement?
Harry Glorikian: And I’ve talked to CEOs of hospitals and they’re like, Oh, we’re we’re doing that all the time. I’m like, If you publish that, they’re like, No, no, no, no. That’s for internal training. We don’t. We don’t. And I’m like, Look, there’s a bell curve of doctors. I really want to know the guys at the upper end of the bell curve, Don’t send me to the guy at the bottom of the bell curve.
But it’s they they’re very close to the vest on that data. And that, I think, would make a huge difference in outcomes for patients.
Ariel Katz: It will mean, that is something that we’re working on and launching next year, both looking at at a hospital and at a doctor level. So is UPMC or is Mt. Sinai better or worse at doing a knee replacement than Duke, who has better patient outcomes? Duke or Yale Cancer Center? Which doctors are good at treating patients with cancer. That’s what everyone needs to know. Pharma companies and patients and all of us want to know that.
Harry Glorikian: That would be huge. That would be a big game changer. Okay, let’s put it this way. It would be a game changer if patients have the information.
Ariel Katz: I agree. They’re going to get it from us via their insurance websites.
Harry Glorikian: That’s awesome. I mean, I look, I’m sold. I’m going to I’m going to start using it myself. So I wish you guys great success. I think this is great information that needs to get out there to the average person. I think it would make a big difference in their outcomes.
Ariel Katz: Yeah, I appreciate that and I thank you for having me on the podcast. That was great.
Harry Glorikian: Excellent.
Harry Glorikian: That’s it for this week’s episode.
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