A (1:15)

Nice. Yeah.

C (1:15)

I didn't know it was haunted. Did you know it's haunted?

A (1:17)

It's. Tell me more.

C (1:18)

My co founder booked the hotel, and then last night I realized it was haunted, and then. Well, I'm kind of superstitious, but he's not. And then he just looks at me like I'm crazy. But why would you book a haunted hotel? Or in a hotel.

A (1:31)

Yeah, exactly.

C (1:32)

That's what happens when you trust some of the legit.

A (1:34)

What happened last night?

C (1:35)

Anything?

A (1:35)

No, nothing weird.

C (1:36)

No, I made sure of it. You did? Got to stay on guard.

A (1:39)

What did you do? Did you bless the room or.

C (1:41)

No, I just prayed.

A (1:42)

You did?

C (1:42)

Yeah, of course.

A (1:43)

Nice. I like that. Yeah. You can never be too careful with these ghosts. They're out there. Yeah. Some of them are angry, but there's some good ones, too.

C (1:51)

They're in disguise.

A (1:51)

They're in disguise. So, co founder. What's his name or her name?

C (1:55)

Kelvin.

A (1:55)

Kelvin, yeah. How did you meet?

C (1:57)

Oh, we actually met, I would say four years ago. Yeah, we both went to NYU for undergrad. And so before we even met, we had come to the same conclusion. We were like, why does the school not have a weightlifting club? Like, people go to the gym. There's no club for people to go to the gym. So a few days after we met, we're like, why don't we just make the club ourselves? And then in our first year, we actually made the club into one of the biggest clubs on campus.

A (2:20)

And the Weightlifting Club.

C (2:21)

Yeah. Go to the gym. I don't know why there's no club for people to go to the gym. Right.

A (2:26)

Yeah.

C (2:26)

And then it's actually nice because we had multiple events where like 50 plus students came out and we had no school support the first year. And this is the best part, where some of the events, they actually had to pay to be there, like 20 bucks. And people still came out because usually people just go for the free food. Right?

A (2:41)

Yeah.

C (2:41)

School events. But yeah, I think that's.

A (2:44)

Yeah. Or the progressives. You guys do progressives?

C (2:46)

What's a progressive?

A (2:47)

Yeah, it's where you. Each room makes a cocktail and you move from room to room drinking different cocktails. It's called a progressive.

C (2:55)

Never done that. Interesting. No, I haven't even heard of it.

A (2:58)

Did you stay in a dorm room or did you have an apartment or.

C (3:00)

No, I stayed in the dorm, but I went to school in New York. I went to nyu, obviously, and then I got my master's at Columbia.

A (3:06)

Oh, Columbia's beautiful.

C (3:08)

Yeah, Columbia is very nice campus. So is nyu, though.

A (3:10)

Where are you from?

C (3:11)

I'm from outside of Philadelphia, but in New Jersey, so. Oh, yeah?

A (3:15)

What town?

C (3:16)

It's called Belmar.

A (3:17)

Belmar.

C (3:17)

Belmar. But not the beach town if you're from New Jersey. Not B, E, L, M, A, R. B, E, L, L, M, A W, R. There's two Belmars.

A (3:25)

Ever been to Ruts Hut?

C (3:26)

What is that?

A (3:27)

It's deep fried hot dogs.

C (3:29)

Where is it?

A (3:30)

It's in. Boy, I think it's not Mawa, but it's up in that area.

C (3:34)

No, Deep fried hot dog does sound interesting.

A (3:37)

That sounds good, doesn't it?

C (3:38)

Yeah. I was born in New York, so technically in New York, we're in New York. Brooklyn.

A (3:43)

Oh, yeah.

C (3:43)

That's where I live right now. I live in Brooklyn.

A (3:45)

Yankees fan?

C (3:46)

No.

A (3:47)

Good. I like you already. Red Sox fan?

C (3:49)

No. Mets fan. Wait until you. Yeah, wait until you see which team I'm a fan of.

B (3:53)

Dodgers.

C (3:55)

Where did I grow up? Where did I grow up?

A (3:56)

Oh, where did you grow up? You're not a Phillies fan.

C (4:00)

Well, I don't watch baseball, so. But if I had to choose a team, that had to be the Phillies. Really, I'm a fanatic.

A (4:06)

Oh, but I don't watch baseball.

C (4:07)

Yeah, I like the Fanatic, though I like the color green.

A (4:10)

Well, Phillies are always good fans.

C (4:13)

Yeah, the Phillies always bombed out of the playoffs. We always.

A (4:16)

Well, the Eagles do well, though only.

C (4:18)

Recently I got to see our first and second super bowl.

A (4:21)

So. You did. So tell me about the company. What do you guys do?

C (4:25)

Yeah, so we basically automate prioritization for enterprise hospital systems. And basically we do it differently though. Our agents as default, they live outside Phi and ehr, which is a novel concept. A lot of healthcare administrators don't really get how we can automate the entire process without phi. Right. Like in order to automate the process, you need access to what the patient specifics are. So yeah, that basically is like the number one thing we do.

A (4:53)

And so how do you do that without phi?

C (4:56)

Oh, okay. So I think the number one thing we realized when working through the issue is that enterprise health systems, I think we all know they're very risk averse and they have a lot of fears. They're like, they don't want you touching their data. They want to know exactly where their data goes. It's valid. Right, but sometimes it goes a little too far. It's like they're like nothing at all. Like any mention and you're out the door. Right. So basically most other companies are building solutions where they automate the entire process. Let's say you integrate into ehr, you from step one, you pull patient charts, you do all the review and then you submit it yourself. Right. But what's nice is hospitals already have teams working on these issues. It's just that they're taking too long with outdated manual workflows. Right. And so these teams can actually use guava to get rid of maybe 90%, 90, 95% of the time consuming steps such as manual policy review, step therapy, validation, coverage checks is a huge thing. So that's basically the specialist has to call into the payer and confirm if the patient's plan, benefits, anything verified, all that, that usually takes like an hour and most of it's on hold. I think that's the infuriating part. They have to stay on hold with an answering machine. So the way we do it is we have AI agents that basically. Let me break it down for you. So basically you receive a referral, Right. And then you need to do a coverage check. We have voice agents that call the payer for you, win a hold for you, handle all the basic steps. And then once human intervention is required, it performs what we call a warm transfer. So it just calls the specialist phone and then it links them together and so now they're talking to a human. So you never have to talk to an answer again.

A (6:33)

Yeah.

C (6:34)

And then medical policy review also takes a long time. I think estimates are like it's 40 to 45 minutes. But I'll give you insight. I think a lot of administrators, they don't really ask where that number comes from. But a lot of reviews. Are prioritization not required. Right. Like basically the drug is not PA is not required, step therapy is not required and they count that towards the 40 to 45 minute estimate. So some prioritizations actually take up to let's say like three to four hours. But because there's like the really simple ones, one minute prioritization is not required. It balances out to around 40, 45 minutes. So medical policy review usually takes around one hour to on average and then we can automate that in about 10 to 15 seconds. Interesting, because instead of you having to search for everything yourself, figuring out how it all fits together in this puzzle, our AI agents already trained on all that knowledge and then generates that information for the specialist to understand exactly what they need to include from the EHR in order to get a good submission.

A (7:29)

Are you in production?

C (7:30)

Yeah, we're currently working with New York Presbyterian, that's our anchor client. But we're also in talks of other like New York City health care systems. Obviously we're based in New York, that's why most of the hospitals we're working with are New York City hospitals.

A (7:44)

Yeah.

C (7:44)

But our long term vision is to be basically the prior authorization automation layer for hospitals across the country. I don't want you to think that we're just in New York.

A (7:55)

When did you start the company?

C (7:57)

We started doing this around I would say three months ago. Oh, so wow. Yeah.

A (8:01)

And you got an A client already.

C (8:03)

Well, hopefully we're in AI governance, but in the last step of AI governance. Nice. We've already been approved for the pilot and then that's great. Obviously AI governance comes with its own set of questions.

A (8:13)

Congratulations. Yeah, yeah. And how did you come up with the idea?

C (8:18)

Okay, so me and my co founder, both of our families are in medicine, my co founder especially, or in the sciences. And so we really wanted to find a way to figure out a solution to make a change in healthcare. And after speaking, we spent so much time, I think people told us not to do it. It's like we spent like a month just talking to people like providers, specialists basically. Not even just providers like doctors, like the people actually doing the administrative work too. And Basically we got a really good sense that obviously we could focus on the manual workflows and that obviously can be automated. And then so basically that put us in touch with the director of infusion at New York Presbyterian. And then he's basically like, yeah, this is all the issues that we're facing and is there anything that can be done? And obviously we're like, yeah, this seems pretty simple to automate. Right. Like I just explained to you how it can be automated. But the issue is once we did it, it actually didn't work because NYP is like, this is not compliant. We don't want any data, we don't want our agents touching anything. So now we're like, okay, so let's start from the beginning. Let's build an even more complicated process. Like I explained at the beginning, I explained the way it's usually done. Pretty simple. And then now we have to do is all these convoluted layers in order to make it so it's absolutely no data is being touched.

A (9:36)

Right, Nice.

C (9:37)

And so we came up with a unique solution and so we can do both. But right now I think we found the way to like access hospitals where we can ease you of your concerns about all these, like, data privacy, whatever. Right?

A (9:51)

Yeah.

C (9:52)

Because we are outside the EHR and we automate like 95% of the Timec consuming steps. All you have to do is type in the drug, type in the payer, the plan and optional diagnosis, and then a minute later you're ready to submit. Wow. So obviously there's still a human in the loop, but you're typing something in and then you're looking over the submission. Obviously you're filling in any phi on your end because we output placeholders where the patient name should go. You know what the patient name is. The specialist can access the ehr, we don't need to access any of your EHR or phi, and then they can do a final check and submit.

A (10:25)

Nice. What have you learned so far over the last couple days here?

C (10:29)

Yeah, I think aimed's been very interesting. I really enjoy the smaller talks. I think the biggest issue, I would have to say is I think people, especially healthcare providers. Right. It doesn't have to be just healthcare providers, it can just be people in general. But obviously it's healthcare providers here is they have a good idea of what AI can do, but they still have a lot of concerns because they don't really see. If they saw what I saw, I don't know about you. If they saw what I saw, I wouldn't have the concerns. I just make sure my ducks are in row instead of being like, I'm not going to take any steps. Obviously. I've been working in an enterprise health with administrators for like three months. I know that's not that much, but even I can see it's like no one wants to be the one that takes the risk because they're like, if it's working as it is, which it's not, we can go into that too. It's not working as it is. They don't want to take the risks to be the one that takes the first step forward.

A (11:25)

Yeah. Speaking of risk, what's the riskiest thing you've ever done?

C (11:30)

The riskiest thing I've ever done? Yeah, I'm really bad at these questions. I'd have to think risk. I'm pretty.

A (11:37)

Never jump out of a plane.

C (11:38)

Oh, I want to jump out of a plane.

A (11:40)

Never.

C (11:40)

I'm a very bungee jump calculated person. If it's 0.1% chance that I don't get to see tomorrow, I'm not gonna.

A (11:46)

Go on a blind date. Ever go on a blind date?

C (11:48)

Of course. Blind dates are.

A (11:49)

That's pretty risky, right?

C (11:50)

Is the blind date like you don't know what she looks like?

A (11:52)

Oh, yeah.

C (11:53)

Oh, no, I wouldn't do that. You gotta be. She has to know what you look like. I'm confident about what I look like.

A (11:58)

And there's apps for that now anyway, right. So.

C (12:00)

Yeah, yeah, we could talk about that too, but.

A (12:02)

Oh, really?

C (12:03)

Yeah, Yeah.

A (12:03)

I don't know if we should go there.

C (12:04)

Okay. Don't worry, we'll stay on top.

A (12:05)

Rated G show.

C (12:06)

Yeah, we'll stay on top. Yeah.

A (12:08)

What Mark, What? Are you going to add anything? My co host here. Why Guava?

C (12:13)

Oh, yeah, Nice. It's a good name, right? Guava.

A (12:15)

I love it. Yeah.

C (12:16)

So people will usually see as a fruit, but actually it's a symbol of integration in some cultures. Right. So I like the name, I like the fruit.

A (12:24)

Yeah.

C (12:24)

But obviously I see it more as it's a connection between, I think healthcare and tech, where that doesn't exist right now. But basically it's like integration and adoption. And guava is basically. And it's seen as that because it's like a universal fruit. It's obviously like it's around the world now and people like it. It's a sign of cultural integration. So I want to make it a tech integration. I love it.

A (12:49)

Go back in time and you could change one decision you made in your life.

C (12:53)

What would it Be I think if I could change one decision in my life, I'd probably, I think I'm actually quite content. I talked earlier about my faith. We don't have to go into that but basically I think everything has led me to this place and I've obviously made mistakes. Right. But I wouldn't say anything I've done that would change the way my life has turned out. I think I'm pretty satisfied with how, where I am.

A (13:16)

I mean it could be anything. I won't eat that burrito next time.

C (13:19)

Going, oh yeah, well that's an easy question then. Okay, so yeah, when I was a kid, this is when I was six, my mom, she Chinese people, we like microwaving milk and then basically I gym first period and then she made me microwave milk. I was not trying to drink it but then she made me drink it and I'm not going to continue the story but you know, no, nothing actually happened but I just felt horrible and then obviously there's a girl I liked and then I was like, don't do anything.

A (13:42)

Did you get sick?

C (13:44)

Yeah, I got sick but nothing happened from the sickness. I just felt and then if I could go back in time, just don't drink that milk. Don't drink the, I mean if we're.

A (13:50)

Going to go don't drink the milk.

C (13:52)

Not major life decision, then I can come up a lot of small stories.

A (13:55)

Yeah, that's good.

C (13:56)

Yeah.

A (13:57)

He's laughing, Mark's laughing. I think if you could go back in time and tell your 20 year old self something, what would it be? Probably was like not a long time.

C (14:05)

Yeah, it's not that long ago. Do I look young?

A (14:07)

You look young. Okay.

C (14:08)

Yeah. So basically.

A (14:10)

But I'm not good with ages, so.

C (14:11)

Okay, you look young too, so.

A (14:12)

Well, thank you sir. Thank you.

C (14:14)

Yeah, don't worry.

A (14:15)

Who do you think's younger?

C (14:17)

If I had to guess? Well, usually when people ask this is a trick question but I'm not going to approach it like that. I'll go, he's younger.

A (14:24)

Yeah, yeah, but you got the.

C (14:26)

That's a question.

A (14:27)

He knew exactly. He knew exactly because he always says like when it's when you ask people, they know.

B (14:31)

The smart ones know.

A (14:33)

Not everyone answers it that way.

C (14:34)

Yeah, you're the intuitive one. Yeah. Like I said, you're smart.

A (14:39)

You're definitely the smartest person at this table, I think. No. Yes, of course you are. Shut stop.

C (14:44)

There's different levels of intelligence.

A (14:46)

What level does he have?

C (14:48)

Social intelligence. Right, Social. Social. And obviously you guys seem pretty successful. And also, I think we are getting off topic, but it's nice to see siblings have great relationships.

A (14:58)

Who said we had a great relationship?

C (15:00)

You guys wouldn't. If you guys think this isn't good relationship, then you haven't seen bad relationships. Right?

A (15:05)

He's okay.

C (15:06)

Yeah, it's.

A (15:07)

I'll keep him around.

C (15:08)

But I could go into basically earlier when I said that hospitals, it's like they want to follow the status quo, right?

A (15:13)

Yeah.

C (15:14)

I don't know how if we're going on time, but I'll do something quick on this. It's basically, I think hospitals are really struggling to retain a lot of their services. I think especially for payers, they're moving a lot of services off site. And this comes with the administrative bloat in health care. Right. I think everyone knows that there's been a lot of layoffs, especially in New York City and across the country in health systems. While hospitals are still. They still are required to scale, they still want to scale. This is one of something crucial to them. So the question I would ask is, for administrators is at a certain point you have to take a risk, right. And you have to ask yourself, is this sustainable to keep going down this direction and following the status quo? And if not, then what is the huge change I need to make in order to make sure this doesn't keep happening?

A (15:56)

I love that. Let's end here. What a great question. Great guest, Nick Lynn Guava. Check out his company. And we're out.

C (16:03)

Thank you.

B (16:09)

Thanks for listening to Risk Never Sleeps. For the show, notes, resources and more information and how to transform the protection of patient safety. Visit us@cincinnat.com that's C-E N S I N E T.com I'm your host, Ed Gaudet. And until next time, stay vigilant because Risk never sleeps.