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In this captivating episode of data driven, we engage in a thought

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provoking discussion about equities of care and disparities in the health

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care system. Our guest explains how patients

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experience varying levels of care based on their unique circumstances,

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an issue that undoubtedly impacts both the health care system and the

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patients themselves. We discuss the administrative

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inefficiencies and lack of effective management in healthcare systems.

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Can data and AI really fix this issue? Listen

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to find out.

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Hello, and welcome to Data Driven, the podcast where we explore the

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emergent. Right? It's no longer emerging. Fields of data science,

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machine learning and artificial intelligence, and, of course, data engineering, which

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is really the foundation for all of the good stuff that we're gonna talk about

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today. How you doing, Andy? I'm well, Frank. How about yourself? I'm

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doing alright. I'm still working on memorizing the season 7 kinda intro,

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which is probably by the time this show goes out, it's It's been a

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little different each time. So Sure. But it's all good. I I like

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the, the emphasis on data engineering, of course. Mhmm. I'm

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Naturally. But, totally

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totally get that. And, I'm really excited about,

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today's guest. Cool. Yeah. Me too. I I will add

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that that before we talk about our guest, that the the emphasis of

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data engineering isn't just for you. It's mostly for you. Well,

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thanks, Frank. I appreciate that. The more work I've done in

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in enterprises, the more I realized that, you know, this is

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It is foundational. Right? And and and if you follow me on on my Red

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Hat channels and things like that, you know, I have this this talk, you know,

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red, rock stars and roadies. Right. Like, our office hours talk, by the way.

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Oh, thank you. Yeah. And, you know, and I use the

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example of Taylor Swift because my niece is a huge fan of hers. Right? So

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people buy the tickets for Tay Tay. Right? But Tay Tay's concert

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wouldn't be awesome. I'm not a fan. Right? So I'm not and they don't

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hate. I don't fan. I'm very neutral. I'm Tay I'm Tay Tay neutral. Right?

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And my my my Tay Tay Probability wave has

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function is not collapsed yet. So, that's

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a quantum computing joke, which you will get or you won't

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get or neither, I suppose, which is another joke. Anyway,

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it really comes up that that, you you know, it's important. Right? Like, the the

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rock star is important. That's who sells the tickets, but Absolutely. The

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performance can't go on without all the people that go in. So roadies.

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Yeah. With that, I'm gonna we're gonna have a great conversation with Joe

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Bethone, or Bethony. I'm not we'll get the corrected pronunciation in a

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minute. He's the cofounder and CEO of Anexis Health, a leading health care

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tech company That helps lessen Nice. Administrative and

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logistical barriers across the patient experience, increase access to

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care, reduce financial burdens at both Patient and provider

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levels. His goal is to change the world by building a culture around health care

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data that people wanna be a part of. Welcome to the show, Joe. Thanks so

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much, Frank. Hey, Andy. Hey. Yeah. It's it's we had a

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little bit of a chat in the virtual green room here, and we'll get to

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that maybe at the, as the show progresses. But, just

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wanna welcome you, Joe, to the show. The, the the

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goals, the mission, of the company just sounds amazing.

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It sounds what exactly is needed. Right? Yeah. So so

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so tell us about how did you how'd you get here? Like, how'd

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you How'd you get there? How'd you hit that

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frustration point? What made you take the action, and then what your firm is

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doing? Yeah. Yeah. I I think that it It took me

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longer to get here than I than it should have. Right? I

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think I'm more stubborn than I ever thought, And

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maybe it just takes me longer to learn. I spent a lot of time in

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the pharma industry doing a bunch of different things.

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So the things that I did in pharma that relate to what I'm doing

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today, were around roles I

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played in the business of oncology. Alright. So

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I did things like create payer focusing teams, facing

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teams, led reimbursement Teams. I led

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relationships in big health care companies. I

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had the opportunity and the blessing To lead

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our relationships with advocacy groups. And

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that was probably one of the most meaningful experiences in my

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professional career Because it got me

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into this seat to watch people that dedicated their

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entire Professional life. 2, advocating on

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behalf of patients. In this case, cancer patients.

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So it was extremely meaningful for me

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To watch people or to interact with people and try

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to play some kind of meaningful role in the

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lives of those that were looking to impact the lives of

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patients with a cancer diagnosis. So that was a really cool

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opportunity. I did learn a lot through all of These

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other experiences, another big experience was

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leading relationships with large health care

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companies. Large health care companies that supported and sold

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into pharma. So what I learned was

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there's a lot of crap out there. Right? And there's some good stuff.

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And learning the ability to kinda

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sift through things, learning the ability to say, hey, this looks really cool,

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But how do you measure it? Right? There's a kind of a data story in

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that. Right? Right. So ultimately,

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I spent too much time because I'm Stubborn,

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and I think that I learn quickly, but sometimes I refuse

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to learn On other's terms. Right? And

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so other's terms, the Andy God, in this case. Right?

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Where there is a there there's a plan in play here. So, Ultimately,

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I don't have any regrets, and, really, the honest truth is is I needed all

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those experiences to help me as an operator. So I stepped out of

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pharma. I did the operator gig somewhere else prior to this

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and it was around data. It was around taking clearing house

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data, bringing it in, And creating meaning and action

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to that date. Meaning so it's clearing us data for

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claims and remittance. And there's a lot of good data in there. Right? But

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it's all how it's packaged. Right? So in the oncology space,

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provider organizations are buy and bill. They have pharmacy dispensing software,

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So they've got skin in the game. Certainly, the patient has skin in the game

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and stuff not getting paid for it is getting paid for. Right? So we

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ingested the data. We looked at it closely. We found out what was going

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on with things that were being denied. Why was it being denied?

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What are the things that we could do to impact Denials or

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approvals or time to payments. Right? And that all impacts patient

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care. So we took that data. We ingested We

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created meeting in action. We created pretty pictures, and we sold in to

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pharma. We help providers, right, help their patients.

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So during that time, as an operator, we

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were discovering and exploring other things to truly have an

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impact on improving access to health care for

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patients. And so at that time, in

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that role as an operator With other partners,

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we actually were working with another software company to start to build the

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bones of our technology at Anexis Health today,

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And that is assist point. We can get a little into a little bit more,

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about that later, but that's the journey that led me to

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Anexis Health. Do we wanna pause and banter or,

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go through some questions, or you want me to tell you a little bit about

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Anexis Health? Well, I'd like to know more about Anexis Health. I mean, there's there's

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a lot to unpack what you said, but I think if we kind of if

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you close the loop and kinda see what you do, that might that might make

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the questions a little more clearer. Yeah. And I think to the audience and maybe

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you guys, depending on how much time that, you

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guys spent just looking at our website, Ultimately, there's a big

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conversation going on right now around equities of care.

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I don't know if you guys have heard about that, but ultimately,

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Whether you call it equities of care or disparities of care,

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there is a real issue in our healthcare system

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where Based on specific circumstances,

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patients are getting care or not getting care, or getting care

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in a different way. Right? And it's having an impact on our health care system.

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It's having an impact on patients. So it's it's really interesting

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that this is a really hot topic. We actually started building

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building this this concept of a company really before

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this became a hot topic. So what we're solving for

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is actually improving the

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way the health care journey is managed. And our focus

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is busting through administrative toxicities That often lead to

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financial toxins. So how do we do that? Yeah. We

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basically provide a comprehensive

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Tooling through technology and through

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services, tech enabled services, to manage

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financial assistance start to finish For provider organizations.

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So it could be a community provider, and we're in 14 different disease

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states now. We started in in oncology. And, ultimately,

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What we do for provider organizations as it relates to financial

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assistance is 4 key pillars, search and roll, track, and adult.

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So if you guys know anything about the health care space and revenue

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cycle management, ultimately, what we do for financial assistance

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is that complete cycle of management around financial

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assistance, and and it does a couple things that are really important. Number 1,

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it makes the providers whole Economically so that they can continue to treat

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patients the way they need to. It takes a burden off

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of the patient, so they get the health care they deserve.

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And, ultimately, we're making this heavy administrative

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process and function cleaner, Easier,

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more automated, more comprehensive, and so that's 1 that's what we

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started. Right? That was the the MVP that we created, And then

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we started to develop other tools around access. And

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so management of free drug is a really important thing in the

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space. It's something that is needed to get patients on the

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therapy that they need, but often, it's a net

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negative To life science, to the provider, even to the patient

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because of what's actually not being paid for wrapped around that. And so

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we do a really good job of Managing that, making sure that where there's an

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opportunity to convert patients to other assistance options to get

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commercial drug, we do that as well. So those things

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that we're currently doing right now, inclusive of making sure that

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everyone has access to other services like travel, Lodging,

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psychosocial, other other services are in one

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destination, in one place. Now Why is

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this a big thing? Because before we started doing this, really, we were the

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1st to market with this provider centric comprehensive way of

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approaching things. It was all spreadsheets. It was fax machines. It

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was post it notes. It was free text notes in EHRs and rev

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cycles. So it was a mess, And that was

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our competition. Now our vision our vision of this organization

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is to bust through in more administrative top systems

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by providing the enterprise platform for provider

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organizations. So it could be community providers, health

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systems, institutions. The enterprise platform

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we that that we wanna provide is focused on administrative logistics. So in

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the health care system, provider organizations have Three key technology

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enterprise platforms, the EHR, the electronic health record,

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the revenue cycle management system, the cash register, And

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and the intake engine and the pharmacy dispensing software.

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All of those systems are not designed to manage

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The administrative logistics of the patient's care journey. So as

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a country, we talk about equities of care. We talk about managing the

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patient experience in their health care journey, and we haven't

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tooled the space to do that effectively. Right? And if

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you think about it, Most of the if you guys have

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individual experience or family experience or friend experience, getting

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a diagnosis of cancer or some

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disease that is associated with the sophisticated disease

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states like gastroenterology, rheumatoid arthritis. I could go on and on. That's a

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heavy piece of information to receive. Right? Oh, it's a life changing

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event. Oh my god. And then they said, go figure it out. I think you

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should get this, But go figure out all these scenarios, like, how you're gonna pay

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for it? Is there help out there? Right? Are you going to have

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to be at a certain, like when we talk about disparities,

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Andy, you live in Farmville, Virginia. I don't know how close the the

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the the really top notch next level

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Center you would go to for a cancer diagnosis, but Yeah. Getting there

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and getting there on a regular basis is something that we struggle with. Right? So

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if you're in rural America or there's other scenarios around Urban America. Anyway,

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there's a lot of administrative functions that aren't managed

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effectively, and it's all one off. So the things that we wanna Really

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carve out and manage from an enterprise approach

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with provider organizations is the remote nature of patients that are

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on oral therapeutics. The cell and gene space is

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heavily laden with administrative logistics, the testing,

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diagnostics, and genomic profiling space. So

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by doing that, we're creating a technology network. We're pretty big

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already with our current state. And by creating that technology

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network, We create the ecosystem by which

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services and data solutions flow through, and pharma invests

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heavily in that To automate, to make sure there's fulfillment

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to impact 3 key things. Getting patients on intended therapy,

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Getting them on therapy quicker and keeping them on therapy because we know

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from our data, when there is an a therapeutic

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designed and there's scientific evidence around the impact it

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has. And if you do those 3 things, you're gonna improve outcomes.

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So That's what we're doing as an organization. I

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I wanna tell you guys that I am a capitalist

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at heart With a purpose to change the world. You said that earlier, and that's

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something that we can be and can do. Well, that's

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true. They're not mutually exclusive, and there's a lot There's a lot

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of unpacking that. Right? And then and just as, you know,

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thank God I never had a cancer diagnosis, but

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just Doing stuff in the health care system. Right? Yeah.

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Just doing anything in the health care system. You look at the mass

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quantities of paperwork that has to go And I mean, not just

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paperwork, like, in the term, but, like, actual paper that's still

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used. Like, you mentioned post it notes and spreadsheets. About the

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fax machine, Frank. Or the fax machine. It's crazy. And

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it's, like, I'm just in my back of my head, like, that little

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data engineer in me is Freaking out because, oh my god, like, that's

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at least it's like there's n number, x number of formats,

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and and and and it just And, you know, people

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of this show know, like, that is that is a huge,

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barrier. Like, that's a and that's a big

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That alone is it will will block it. And you kinda look at it like,

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can't this be better? And, you know, and I kinda see, like, oh, yeah. You're

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still using fax machines. Wow.

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Yeah. So so it's kinda interesting where when I

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was talk talked to you about How how we start and what we're doing

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around this equities of care thing. And

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what's going on in this space is

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It's very similar to Frank, Andy, how you describe

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on, your website around the what's

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going on with data Related to the old what

259
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went on with oil. Right? So when we think about

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equities of care, when we think about the health care Space.

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What you guys describe around the data opportunity

262
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is incredible. I think about it on a couple different levels. Number

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1, You can tell I'm passionate about what I'm doing. Right? Absolutely. And

264
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you can tell that hopefully,

265
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you Stan, right, I'm on this program because data is really important to me. But

266
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it's super important to me for a different reason than it is to you guys.

267
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And I think But there's some similarities. Right? Where Oh, sure.

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Data make data makes a freaking difference. Right? And so for me,

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to sit here and tell you my story, Or to sell into life science, or

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to sell into a provider, or sit on another podcast that I was

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on yesterday and describe how we're impacting the market,

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I have to have data to tell the story to be able

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to prove that I can, actually, in my company, right,

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can improve the likelihood that a patient's gonna get on therapy, and we do.

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Right? To make sure that we're demonstrating with what we do, we're getting patients on

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therapy quicker. We're getting patients on therapy and maintaining them on

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therapy. So in everything we do, we're constantly analyzing

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the data to demonstrate that we're doing those things. But, Frank, I

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think you said something earlier, or maybe we're talking offline about, like,

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the the whole idea of well, no. You just said it. Right? Like, the

281
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cumbersome nature of the paperwork Process. Yeah. Right? I think

282
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about some of the things that we do. Right? We wanna we wanna get rid

283
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of the spreadsheets. We wanna get rid of the paper. But sometimes we

284
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can't do it fast enough. Right? So We're constantly

285
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iterating on our technology. So the iteration in our

286
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technology has to include machine learning and AI and RPA.

287
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And you know what? You don't invest in that heavily, if you don't understand it

288
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deeply, then you're gonna spin and spin

289
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and spin. And then, ultimately, my unit economics Don't improve because I got

290
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people doing stuff that they shouldn't be doing, processing paper or

291
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manually entering something in the system that should be

292
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automated. So I, anyway, besides the fact that I'm really

293
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passionate about what I do, when I started to read about you guys and listen

294
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to some of your podcasts, you got me, you got me all fired up. Now

295
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I wish you Oh, cool. This Friday because I was ready to roll. Oh, that's

296
00:18:18,995 --> 00:18:22,675
awesome. Thank you. Thanks for that feedback, Joe. And I know I've had

297
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a Couple of experiences that I could, you kinda

298
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relate to what you're talking about. 1, I, worked for

299
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Unisys for a couple years, And we did Medicaid,

300
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systems, MMIS systems. And so you've got

301
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kind of that it it's a bit of a fringe element or at least people

302
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kinda think of it that way. It's not quite like,

303
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the big insurance companies and stuff, and I I've done work on their data as

304
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well, helping them with data engineering there. That's that's what I

305
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do. But it was very eye opening to

306
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see, You know, to see and hear the stories and

307
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managing a team that grew to 40 people. That was new

308
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for me as well, but the key was Picking a story and

309
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going with that and saying it was a it was a pharmacy story. And

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it was sometime in the future after we deploy this,

311
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Some grandmother is gonna go and try at 10 minutes to 5 on

312
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a Friday, afternoon, and Monday's a holiday. She's

313
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gonna try and fill her prescription. And if she doesn't fill

314
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it, she doesn't know this, but if she doesn't fill it, she's not gonna make

315
00:19:29,525 --> 00:19:33,370
it till Tuesday. And so our job is to make

316
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sure that that grandma gets her prescription in plenty of time before

317
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the pharmacy closes at 5, and that she gets to live for

318
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however long she's supposed to and enjoy her life and have her children and

319
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grandchildren, you know, enjoy her life. And

320
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Pausing in the middle of a presentation of that and just watching

321
00:19:52,230 --> 00:19:55,050
the, you know, they, at this point, we were all remote

322
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watching the faces on the screens And everybody kind of got that

323
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and it resonated. And it's your the stories you're

324
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telling, I I feel are in alignment with that. You're you're focused

325
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on the outcome. And the outcome is people's lives are

326
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better or longer or both. The other personal

327
00:20:14,920 --> 00:20:18,765
story is my my dad passed away in 2019, and

328
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he lived in Appalachia, so a couple 100 miles west of

329
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here. And, you know, I've I've done,

330
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I've been very honored to to be able to go and do

331
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missions work in Honduras. I have few few mission trips down

332
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there. And that area out there is not it

333
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it's it's way closer to Honduras than it is to where I was. I've been

334
00:20:41,815 --> 00:20:44,340
in Farmville. It's just It's neglected,

335
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almost. And and people are struggling even now,

336
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and they were back then as well. And but yet and still,

337
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there were there were good hospitals there. There were doctors there that were caring. I

338
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met with them, talked with them. They were doing their level best. But

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When I mentioned that I did data after we had, had

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built a little bit of trust, when I I worked on data, they they

341
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kinda opened up to me, and they were describing Problems similar to what

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you're solving. The EHR systems that they just

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hit a blocker. And and this is not a complaint. Everybody's doing their

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best. It just wasn't there yet. And

345
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what you described sounds like a big part of the

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solution that a a conversation I had with 1 of dad's docs.

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So that's it's amazing. I think that there are companies out

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trying to solve that. And the technology has gotten so

349
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ubiquitous and connectivity, so ubiquitous and and the

350
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tech's so cheap that it's while it looks

351
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like a large jump from filling in the forms or typing in Excel

352
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To working on a tablet, it's really not anymore. So Well, I

353
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I I I I would take the contrarian point of view of that because, like,

354
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it Okay. It

355
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I I I wonder, like, it's very easy as a technologist,

356
00:22:02,780 --> 00:22:06,515
right, to just assume everybody's gonna Comfortable with technology. Right? You're not wrong,

357
00:22:06,515 --> 00:22:10,195
Andy, but it's just about the patients, mind you. My dad, Andy, my

358
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tablet? No. Probably even the medical even the doc,

359
00:22:13,900 --> 00:22:17,740
The medical field is a field that is driven has always been driven

360
00:22:17,740 --> 00:22:21,525
by data. Right? Like Yeah. You know? But they are not Data

361
00:22:21,525 --> 00:22:24,885
driven. And, you know, that's not a plug for the podcast, but, like, it just

362
00:22:24,965 --> 00:22:28,725
it's like you know, for instance, right, and and a more lighthearted thing. Right?

363
00:22:28,725 --> 00:22:32,070
This that's not life and death. Right? I have a Fitbit. I I I track

364
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my heart rate. I I I, you know, I I track a lot of data

365
00:22:35,350 --> 00:22:39,049
and track my weight. I go into my doctor and,

366
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You know, I show it was like a group practice. I showed her, like, you

367
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know, this is the data, like, you know. Because, you know, you look at my

368
00:22:45,375 --> 00:22:49,140
profile, my age, my height, my weight, like, You know, but I'm like,

369
00:22:49,140 --> 00:22:52,920
here's my resting heart rate. Here's all the data I have on my heart rate.

370
00:22:53,140 --> 00:22:56,905
Yeah. And she just looked at me like I was insane. And I'm

371
00:22:56,905 --> 00:23:00,665
probably you're definitely an you're not insane. But you are an anomaly, I think, that

372
00:23:00,665 --> 00:23:04,370
you not only track it, but you present it, to your

373
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doctor. So But, you know, you think of that you know, it just seems like

374
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some doctors, I think, are more open to the idea.

375
00:23:12,085 --> 00:23:14,965
Well, I guess there's multiple angles to this. This is probably why it's a difficult

376
00:23:14,965 --> 00:23:18,805
problem to solve. Right? Because there there's there's Yeah. So many players, but everybody

377
00:23:18,805 --> 00:23:22,650
has their own different systems. Right. And, you

378
00:23:22,650 --> 00:23:26,090
know, I I just I just can't I mean, this is there's so much to

379
00:23:26,090 --> 00:23:29,390
unpack. Right? Like, because I can't imagine Yeah. You're getting a some kind of

380
00:23:29,615 --> 00:23:33,075
Bad diagnosis, and

381
00:23:34,015 --> 00:23:37,294
that's a life changing event. And then you're thrown all of this stuff, like, you

382
00:23:37,294 --> 00:23:40,620
know, like like like, all the things that That Joe had said, it's just like

383
00:23:40,860 --> 00:23:44,620
Yeah. You know, how I mean, on a normal like, just a

384
00:23:44,700 --> 00:23:47,660
like, I had a knee surgery, like, 10 years ago. I mean, the the knee

385
00:23:47,660 --> 00:23:51,415
surgery, was 3 months from, you know,

386
00:23:51,415 --> 00:23:54,535
doctor said you need to do it to what was done. And then, like, 3

387
00:23:54,535 --> 00:23:57,820
years later, I'm still getting paperwork, Like, oh, this has to be paid. Like and

388
00:23:57,820 --> 00:24:01,440
it's just like, I I just can't imagine,

389
00:24:01,500 --> 00:24:05,155
like, it just I don't know. Like, that just blew my mind. Like,

390
00:24:05,155 --> 00:24:08,835
3 years later, like, I I I I I half expect to get email

391
00:24:08,835 --> 00:24:12,295
at mail at the new house, like, hey, you still owe us $5

392
00:24:12,435 --> 00:24:16,080
and A $50 processing fee, which, again, like,

393
00:24:17,100 --> 00:24:20,940
is so bizarre. It's it's interesting too

394
00:24:20,940 --> 00:24:24,715
what's going on in the space, so So, actually, to both of your points.

395
00:24:24,715 --> 00:24:28,475
Right? So we've come very far. Mhmm. I just think about some of the

396
00:24:28,475 --> 00:24:32,220
things that the federal government does that's A colossal failure, and then I think about

397
00:24:32,220 --> 00:24:35,740
some things that are favorable. So let's stay positive. Right? Sure. So as it

398
00:24:35,740 --> 00:24:39,465
relates to system and it relates to data, You know, the

399
00:24:39,465 --> 00:24:43,065
federal government launched this meaningful use thing

400
00:24:43,065 --> 00:24:45,885
years years ago, and the health care system

401
00:24:46,505 --> 00:24:50,300
battled. Providers battle over this, being forced

402
00:24:50,300 --> 00:24:54,060
into using an electronic chart system,

403
00:24:54,060 --> 00:24:57,644
right, versus This paper, you still go into

404
00:24:57,644 --> 00:25:01,245
these practices and you still see they've got their manila folders up

405
00:25:01,245 --> 00:25:05,059
on on the wall. And, ultimately, they

406
00:25:05,220 --> 00:25:08,580
it was initially a carrot, and then it became a stick, and there was a

407
00:25:08,580 --> 00:25:12,425
lot of complaining. And there was some heavy economic toll. Right? Because the

408
00:25:12,425 --> 00:25:16,185
amount of money that the federal government was giving through meaningful use wasn't

409
00:25:16,185 --> 00:25:20,025
meaning. Right? Mhmm. But provider organization struggle for what we're all

410
00:25:20,025 --> 00:25:23,580
better for. We all are better better for it, and

411
00:25:23,580 --> 00:25:27,260
we're better for it because of data. But we still struggle. Right? Because if

412
00:25:27,260 --> 00:25:31,095
you're not capturing this data in a way Through or the data

413
00:25:31,095 --> 00:25:34,535
is an input in fields? You guys know. Right? You're not

414
00:25:34,535 --> 00:25:38,375
capturing structured data. Right? You're capturing Right. Free text notes. What the hell do

415
00:25:38,375 --> 00:25:41,440
you do with that? And I don't care how good you think you are at

416
00:25:41,440 --> 00:25:45,200
natural language processing. There's still people you gotta apply to it and still

417
00:25:45,200 --> 00:25:48,695
ineffective. Right? Right. So I just think about that movement

418
00:25:48,915 --> 00:25:52,674
and and how it's getting much better, and we are trying to force everyone to

419
00:25:52,674 --> 00:25:56,215
think about forcing this in the field. Let's understand the fields. Right?

420
00:25:56,460 --> 00:25:59,900
Because the next level of what the federal government has done

421
00:25:59,900 --> 00:26:03,580
very positively, again, is interoperability. So meaningful

422
00:26:03,580 --> 00:26:07,265
use became interoperability. Now everybody's talking about shell

423
00:26:07,345 --> 00:26:10,945
sharing health care data that we used to put walls

424
00:26:10,945 --> 00:26:14,659
around. And that's, yes, such crap They were putting walls

425
00:26:14,659 --> 00:26:18,419
around that, and the primary reason was because people wanted to make money. Right?

426
00:26:18,419 --> 00:26:22,260
Mhmm. So the federal government has decided that this health care data is the patient's

427
00:26:22,260 --> 00:26:25,924
data. Let's figure out a way to share data through a standard

428
00:26:25,924 --> 00:26:29,705
API approach. Right? And Right. Although we're way far from that,

429
00:26:30,010 --> 00:26:33,530
It still has actually made the conversation easier. It's made it

430
00:26:33,530 --> 00:26:37,050
easier for Nexus Health to do the data

431
00:26:37,050 --> 00:26:40,405
thing To allow us to do what we do from a tech and services

432
00:26:40,405 --> 00:26:44,085
perspective much easier and much better because I'm talking to these EHR

433
00:26:44,085 --> 00:26:47,680
systems, these rev cycle systems, these pharmacy dispensing systems,

434
00:26:48,300 --> 00:26:51,740
these data aggregators or institutions or whatever it may

435
00:26:51,740 --> 00:26:55,355
be, and where they may have sat in a place where they're like,

436
00:26:55,355 --> 00:26:59,115
no. You can't come anywhere near my data because I would have

437
00:26:59,115 --> 00:27:02,840
monetized it, and they had no interest in changing the world. Right.

438
00:27:02,840 --> 00:27:06,120
But then Right. To change the world by putting money in their pockets so they

439
00:27:06,120 --> 00:27:09,880
could go spend it somewhere else. Right? So now I think the the industry is

440
00:27:09,880 --> 00:27:13,544
looking at it a little differently, And it's opening

441
00:27:13,544 --> 00:27:17,065
up. I think getting back to the original point that I wanted to make, though,

442
00:27:17,065 --> 00:27:20,445
I think things are getting better. I think it's light new day

443
00:27:20,720 --> 00:27:24,480
Different than it was, like, 10 years ago, but we still have basic challenges.

444
00:27:24,480 --> 00:27:27,780
Right? Like, I think about one of the things that we're facing right now,

445
00:27:28,240 --> 00:27:31,885
where I need to create a

446
00:27:31,885 --> 00:27:35,345
better system by which we connect with charitable foundations

447
00:27:35,885 --> 00:27:39,710
because it's really bulky, what we're solving for there. The charitable

448
00:27:39,710 --> 00:27:42,910
foundations are a five zero one c three. They don't have the people, the time,

449
00:27:42,910 --> 00:27:46,290
the FTEs to engage in some basic conversations.

450
00:27:46,865 --> 00:27:50,225
Like, we make things really easy. And if anybody's listening from charitable

451
00:27:50,225 --> 00:27:53,745
foundations, please contact Sunexis Health because we can make your life

452
00:27:53,745 --> 00:27:57,300
easier. We could make you do better at what you do. You're gonna get more

453
00:27:57,300 --> 00:28:01,140
money in because that we improve it, and we're gonna cost you less money by

454
00:28:01,140 --> 00:28:04,625
the way you process things. But ultimately, people are resistant. There's not a lot of

455
00:28:04,625 --> 00:28:08,385
time. So I'm trying to RPA things. Right? But if I've got an

456
00:28:08,385 --> 00:28:12,010
unwilling participant because They think it's gonna be too much,

457
00:28:12,010 --> 00:28:15,769
or they think there's gonna be security risks. And they're not even willing to

458
00:28:15,769 --> 00:28:19,285
have the conversation that if I wanna do The really clean,

459
00:28:19,285 --> 00:28:22,745
easy way without their participation, they got

460
00:28:22,965 --> 00:28:26,630
stuff blocking my box. And alls I wanna do is the right

461
00:28:26,630 --> 00:28:30,150
thing for the patient. Right? So, yeah, we can we can solve for that and

462
00:28:30,150 --> 00:28:33,774
do that. But guess what? You know how much money we're Spending to solve for

463
00:28:33,774 --> 00:28:37,375
this and iterate on this thing. And ultimately, every penny I

464
00:28:37,375 --> 00:28:40,835
spend on that kind of crap, and it makes it more expensive for me,

465
00:28:41,190 --> 00:28:44,890
Then it makes it harder for me to impact patients. So there's

466
00:28:45,590 --> 00:28:49,030
Every cycle every cycle you spend doing that is a cycle you're not

467
00:28:49,030 --> 00:28:52,465
spending. Sorry, Andy. I got you off. That's okay. No. And and you've got

468
00:28:52,845 --> 00:28:55,985
some well intentioned regulation, HIPAA,

469
00:28:56,740 --> 00:28:59,860
And and just other privacy concerns, just general,

470
00:29:00,180 --> 00:29:03,780
personally identifying information type stuff that that's out

471
00:29:03,780 --> 00:29:07,435
there. And it all collides, I think at at medical

472
00:29:07,435 --> 00:29:11,195
data. So, you know, I while I understand some of

473
00:29:11,195 --> 00:29:14,475
the resistance that you're seeing and I and I kinda get where it's coming from

474
00:29:14,475 --> 00:29:18,108
and and agree with that Part of it, there's ways

475
00:29:18,108 --> 00:29:21,870
to de identify data and and go after aggregates,

476
00:29:21,870 --> 00:29:25,685
especially if you're Trying to do what you're trying to do. You're trying to smooth

477
00:29:25,685 --> 00:29:29,525
out the system for everyone. You're not while while it will

478
00:29:29,525 --> 00:29:32,905
benefit individuals, you're not going after individual data.

479
00:29:33,270 --> 00:29:36,309
So and there's other things. Right? Like, you just gotta make sure. And you do

480
00:29:36,309 --> 00:29:40,070
have to put some time into it. Right? Like, isn't excess help legit? Right?

481
00:29:40,070 --> 00:29:43,785
Well, there's things that can understand if they're legit or not. Are you SOC

482
00:29:43,785 --> 00:29:47,385
2? Are you HITRUST? Right? So there's things that we are

483
00:29:47,385 --> 00:29:51,030
doing from a regulation perspective That make it easier for us to

484
00:29:51,030 --> 00:29:54,870
trust those that wanna prevail in the data. So do you think

485
00:29:54,870 --> 00:29:57,610
that people or organizations hide behind regulations

486
00:29:59,154 --> 00:30:02,995
by saying, like, oh, that's that's HIPAA. We we can't do that, or

487
00:30:02,995 --> 00:30:05,794
it's too much of a risk. Is that is that a thing? It was like

488
00:30:05,875 --> 00:30:09,720
I think, we I think we can all agree that HIPAA has been has

489
00:30:09,720 --> 00:30:13,400
its use. Right? It has its purpose, but I don't understand. Has that been a

490
00:30:13,400 --> 00:30:17,065
has that been a have people used that as blockers? For Unequivocally,

491
00:30:17,605 --> 00:30:21,285
yes, Frank. Unequivocally, yes. Yeah. And and I and

492
00:30:21,285 --> 00:30:24,649
it might it could be It's anywhere. It's

493
00:30:24,649 --> 00:30:28,350
anywhere. It it is and and I think about the segments of our business. Right?

494
00:30:28,570 --> 00:30:32,345
So working with a large Health system

495
00:30:32,345 --> 00:30:35,865
or institution. Right? And and my people are

496
00:30:35,865 --> 00:30:39,245
dealing with 3 lawyers, 2 security

497
00:30:39,465 --> 00:30:43,030
folks, 3 compliance folks, 4 regulators, and

498
00:30:43,230 --> 00:30:47,010
Wow. They all have a really important purpose in this. Mhmm.

499
00:30:47,230 --> 00:30:50,475
But If their purpose is to say no

500
00:30:51,415 --> 00:30:55,095
and Mhmm. Say no, hiding

501
00:30:55,095 --> 00:30:57,140
behind Regulations

502
00:30:58,800 --> 00:31:02,640
and policies versus Yeah. Understanding the reason for that

503
00:31:02,640 --> 00:31:06,335
regulation and Policy and being solution oriented? Oh my

504
00:31:06,335 --> 00:31:09,855
gosh. Yeah. So it blocks or cost time and money at the

505
00:31:09,855 --> 00:31:13,580
provider on the same thing in the life science world, Right? Where

506
00:31:13,580 --> 00:31:17,260
you have lawyers that they're there to do the job. They they have to.

507
00:31:17,260 --> 00:31:21,075
Right? Because no life science company OIG coming down on them for

508
00:31:21,075 --> 00:31:24,915
whatever the reason, but you have to be solution oriented. You gotta look

509
00:31:24,915 --> 00:31:28,610
at the organization. You gotta make sure that organization is

510
00:31:28,610 --> 00:31:32,450
actually doing what it's saying it's doing, and there's

511
00:31:32,450 --> 00:31:35,890
ways to test that and look into that, versus saying no. And and and that's

512
00:31:36,049 --> 00:31:39,725
like, you can you can hear, Frank, The passion and my answer on that question?

513
00:31:39,945 --> 00:31:43,085
Oh my gosh. I've spent so much time and so much money

514
00:31:43,465 --> 00:31:47,200
getting through Those that sit in a seat that they're hiding

515
00:31:47,200 --> 00:31:50,560
behind something because it's easier to say no than to work to a

516
00:31:50,560 --> 00:31:53,700
solution. Yeah. That is that is true.

517
00:31:54,315 --> 00:31:58,155
What are your thoughts about the fire? The, f

518
00:31:58,155 --> 00:32:01,934
h I r. Is it pronounced fire or is it fear? Because I've heard both.

519
00:32:02,640 --> 00:32:06,240
I say fire, and I'm using the authority on the way things are

520
00:32:06,240 --> 00:32:09,760
said. But what's

521
00:32:09,760 --> 00:32:13,595
fascinating is so so the previous job, I, you know, I

522
00:32:14,135 --> 00:32:17,975
I was, technology architect for data and AI at at

523
00:32:17,975 --> 00:32:21,750
at one of the MTCs, Microsoft Technology Centers, And I had never heard

524
00:32:21,750 --> 00:32:25,450
of fire. Right? I've been out of the, electronic health record space

525
00:32:25,510 --> 00:32:29,325
since 2006, and so I it was completely

526
00:32:29,325 --> 00:32:32,365
new to me, and I would blow my mind. It shouldn't blow my mind, but

527
00:32:32,365 --> 00:32:36,179
it was like, wow. This is actually a couple things blew Wow. People

528
00:32:36,179 --> 00:32:40,020
actually in this space got together to work together? That blew

529
00:32:40,020 --> 00:32:43,799
my mind. 2, it was both, Java's, JSON

530
00:32:43,860 --> 00:32:47,285
and XML compliant, Which I thought was pretty pretty cool.

531
00:32:48,385 --> 00:32:51,745
Has that helped? Is that is that been a good kind of kind of having

532
00:32:51,745 --> 00:32:54,805
a common language for these systems to talk to one another?

533
00:32:55,650 --> 00:32:59,430
Yeah. So I think, generally speaking let's just step back. So so fire,

534
00:33:00,210 --> 00:33:03,170
I think there's a lot of conversation around it because that's how the federal government

535
00:33:03,170 --> 00:33:06,705
has decided to put language around the standards. Right? The

536
00:33:06,705 --> 00:33:10,385
standard for communicating. So fire API. So I think just the

537
00:33:10,385 --> 00:33:14,200
API Approach is yes. It's

538
00:33:14,200 --> 00:33:17,560
all good. Right? So let's standardize the way we're gonna share

539
00:33:17,560 --> 00:33:21,295
information in a safe private matter. So, yes, it has changed the

540
00:33:21,295 --> 00:33:25,055
game, but the whole discussion around FHIR and creating FHIR APIs and

541
00:33:25,055 --> 00:33:27,955
standards of communication has opened the door

542
00:33:28,415 --> 00:33:31,450
incredibly to this

543
00:33:31,830 --> 00:33:34,970
idea that we should be really investing

544
00:33:35,670 --> 00:33:39,125
in sharing data and we can't Sit in a

545
00:33:39,125 --> 00:33:42,845
position of hiding behind something or just being scared to death. So Yeah.

546
00:33:42,965 --> 00:33:46,429
I think that the the the conversation, And I think the

547
00:33:46,429 --> 00:33:50,110
work being done and I think the work being done, and then, absolutely, the

548
00:33:50,110 --> 00:33:53,825
application. Right? So whether we describe that we're Using

549
00:33:53,825 --> 00:33:57,345
a FHIR API or we're using an API approach

550
00:33:57,345 --> 00:34:01,105
that we've created the specs around or we're working with someone else's

551
00:34:01,105 --> 00:34:04,940
specs, A whole idea that you can communicate between between

552
00:34:05,340 --> 00:34:08,945
with these application programming interfaces is

553
00:34:09,105 --> 00:34:12,945
Life changing. Right? Because there's still a lot of stuff that we're doing through h

554
00:34:12,945 --> 00:34:16,545
l seven, ADT feeds. There's stuff that we're absorbing in a

555
00:34:16,545 --> 00:34:20,380
flat file. Right? And I can't wait for the day that all of it is

556
00:34:20,380 --> 00:34:24,219
through API. Why? Right. Because it's gonna be better. It's gonna be cleaner. It's gonna

557
00:34:24,219 --> 00:34:27,875
be faster, And it's god, I hope gonna be less

558
00:34:27,875 --> 00:34:31,714
expensive. Well, yeah. I mean, you're right. Go ahead, Frank.

559
00:34:31,714 --> 00:34:34,614
Sorry. No. Anytime you say flat file, it's kinda like,

560
00:34:37,380 --> 00:34:41,000
Yeah. I started to sweat. I said it. I start my palms are sweating.

561
00:34:41,060 --> 00:34:44,885
Right? I I had those sinking feeling in my stomach. So I'll get the

562
00:34:44,885 --> 00:34:48,725
task Andy's memory. We had a guest that made an analogy, and I only remember

563
00:34:48,725 --> 00:34:52,120
part of it where it was basically Data data professionals

564
00:34:52,500 --> 00:34:55,940
were, used to be kind of guardians or

565
00:34:55,940 --> 00:34:59,620
gatekeepers, and now they're shopkeepers. I forget who said

566
00:34:59,620 --> 00:35:02,615
that. That's Donald Farmer. That's what I thought.

567
00:35:03,474 --> 00:35:06,475
I wasn't sure. So so we had a show, and he's like he kinda has

568
00:35:06,595 --> 00:35:10,349
wax is very philosophical because data at one point was Hidden

569
00:35:10,490 --> 00:35:14,329
within an enterprise. There were silos were considered normal. But I think

570
00:35:14,329 --> 00:35:18,175
now what you're talking about with all these different parties, like Like you said,

571
00:35:18,175 --> 00:35:21,715
they have to open it up. They have to be like a store almost. Obviously,

572
00:35:22,095 --> 00:35:25,935
with HIPAA and things like that, there has to be some constraints, but Yeah. But

573
00:35:25,935 --> 00:35:29,590
I think it's a mindset shift. My mindset shift. I didn't

574
00:35:29,590 --> 00:35:32,890
say that. What I was gonna say. Yeah. It's definitely a mindset

575
00:35:32,950 --> 00:35:36,715
culture switch. And I like you, I cringed a little bit

576
00:35:36,715 --> 00:35:40,095
when I heard an EDI, you know,

577
00:35:40,555 --> 00:35:44,300
specification. And I believe h l seven was one

578
00:35:44,300 --> 00:35:48,060
of those. I've it's been a while, but, yeah, those were hard those

579
00:35:48,060 --> 00:35:51,875
are hard to load. And I just did a recent blog post related

580
00:35:51,875 --> 00:35:55,415
to that. I I titled it h, sorry, XM held.

581
00:35:56,035 --> 00:35:59,540
XM held. This

582
00:35:59,540 --> 00:36:03,300
is, you know, give me anything, something,

583
00:36:03,300 --> 00:36:06,980
anything other than XML. But But that but as we're

584
00:36:06,980 --> 00:36:10,785
having that conversation, The different formats and the ways that we, the company,

585
00:36:10,785 --> 00:36:14,545
a data company so we're we're a health care solutions company, but we're a

586
00:36:14,545 --> 00:36:17,685
data company at heart. Right? Right. So I think about

587
00:36:18,670 --> 00:36:22,110
How we wanna be better every day around ETL. Right? So

588
00:36:22,110 --> 00:36:25,570
extract, transform, and load. And and I just think about

589
00:36:25,950 --> 00:36:29,585
the pain that we've been through, The process that we've been through,

590
00:36:30,045 --> 00:36:33,805
but the whole idea is that if we're gonna do this data

591
00:36:33,805 --> 00:36:37,280
thing right, Right? Yeah. And we are there's gonna be

592
00:36:37,280 --> 00:36:40,960
individuals. There's gonna be companies that are gonna invest in this. Like,

593
00:36:40,960 --> 00:36:44,155
there's a lot. There's a lot to doing this. There's a lot to being in

594
00:36:44,155 --> 00:36:47,995
this game for us, right, as data people. And I'm

595
00:36:47,995 --> 00:36:51,620
not a real data person. Right? I'm a I'm a I'm I'm

596
00:36:51,620 --> 00:36:55,300
a I'm a change the world geek, and and so you guys ask questions, I

597
00:36:55,300 --> 00:36:59,060
think, typically, in our website, like, what brought you to data? It's because

598
00:36:59,060 --> 00:37:02,415
data is the only way that I wanna prove that we can change the world

599
00:37:02,415 --> 00:37:06,175
and then how we do it better. But this whole idea that we've gotta invest

600
00:37:06,175 --> 00:37:09,810
so much money And extract, transform, and load.

601
00:37:10,110 --> 00:37:13,550
Right? I don't care. Like, I'm I'm gonna I'm gonna say this to demonstrate I

602
00:37:13,550 --> 00:37:17,070
don't know what I'm talking about. But the amount of money we're investing in palaces

603
00:37:17,070 --> 00:37:20,434
and lakes and Rivers and whatever the hell that is for ETL.

604
00:37:20,815 --> 00:37:24,494
Right? It's crazy. Like, let's just get ourselves wrapped

605
00:37:24,494 --> 00:37:27,869
around the fact that Figuring out the best way

606
00:37:28,490 --> 00:37:31,790
to share data, figuring out the best way to ingest

607
00:37:31,849 --> 00:37:35,625
data and be able to study it in a meaningful way Yeah.

608
00:37:35,625 --> 00:37:38,905
Changes the world, and that's what I wanna do. And I that's why I love

609
00:37:38,905 --> 00:37:42,540
what I see that you guys are doing. Right? Oh, thanks. Well, thanks.

610
00:37:42,540 --> 00:37:46,060
Yeah. This is an awesome conversation. We can go on for another

611
00:37:46,060 --> 00:37:49,820
hour, but, probably good times. Yeah. It's all fun at the

612
00:37:49,820 --> 00:37:51,265
moment. Well, I'm on these guys

613
00:37:55,105 --> 00:37:58,144
So you kinda touched upon the first question we asked, which is how did you

614
00:37:58,144 --> 00:38:01,250
find your way into data? Did you find data or did data find you?

615
00:38:02,130 --> 00:38:05,170
You know what? And and and I I let out some of the stuff that

616
00:38:05,170 --> 00:38:08,815
I pre prepared, but, like, literally all good. Yeah.

617
00:38:08,815 --> 00:38:12,655
Literally, I, for the longest time, knew I

618
00:38:12,655 --> 00:38:14,975
wanted to change the world. I just didn't know how the heck I was gonna

619
00:38:14,975 --> 00:38:18,090
do it. Right? Yeah. So as I started to think about things and I started

620
00:38:18,090 --> 00:38:21,930
to look at things, data points are really like, I think

621
00:38:21,930 --> 00:38:25,665
in bullet points. I think in spreadsheets. Not even knowing that I was a

622
00:38:25,665 --> 00:38:29,265
data guy, I tell our data scientist lead all the time that,

623
00:38:29,505 --> 00:38:32,464
you know, that's what I wanna do next. I want to learn how to be

624
00:38:32,464 --> 00:38:35,950
a data scientist. Right? Because I'm nowhere near that. Like, I gotta go to probably

625
00:38:36,650 --> 00:38:40,490
50 years of slow learner, 50 years of schooling to to be that. But it

626
00:38:40,490 --> 00:38:43,855
was it was this this thing that's changed the world geek inside of

627
00:38:43,855 --> 00:38:47,455
me that let me know I was constantly absorbing data

628
00:38:47,455 --> 00:38:51,130
points. And so once I cited or

629
00:38:51,130 --> 00:38:54,970
once God laid on my heart what I actually wanted to do. Right?

630
00:38:54,970 --> 00:38:58,270
Then it became really clear to me

631
00:38:58,625 --> 00:39:02,145
The data is the way to do it. Right? Data is the way to decide

632
00:39:02,145 --> 00:39:05,665
what you wanna do. Data is the way to decide you is the way to

633
00:39:05,665 --> 00:39:09,340
decide How you build it better, how you structure it. Data is the way

634
00:39:09,340 --> 00:39:13,180
you prove what you've intended to do is working, and then

635
00:39:13,180 --> 00:39:16,465
data will tell you what to ship and what to change to. So, Anyway, I

636
00:39:16,465 --> 00:39:20,145
was brought to it, but I guess in my heart, I always was that kind

637
00:39:20,145 --> 00:39:22,945
of data geek. I just need to learn a lot more to be as cool

638
00:39:22,945 --> 00:39:26,750
as you guys. Oh, We're constantly learning

639
00:39:26,750 --> 00:39:30,050
too. It keeps changing on us, Joe, so it's all good.

640
00:39:30,670 --> 00:39:34,350
Our second question is which go ahead. I love the story. I love the

641
00:39:34,350 --> 00:39:38,175
story in data. Right? Oh, yeah. There are there are stories in

642
00:39:38,175 --> 00:39:41,695
every data point. Ab absolutely. And that's a really good way to

643
00:39:41,695 --> 00:39:45,370
kinda get everybody on the same page. We've been listening to stories

644
00:39:45,370 --> 00:39:49,070
since, you know, we were kids, so, you know, that helps.

645
00:39:49,610 --> 00:39:53,070
Our our second question is, what's your favorite part of your current gig?

646
00:39:55,605 --> 00:39:58,085
When I go back to the stuff I'm saying over and over again, it's making

647
00:39:58,085 --> 00:40:01,445
a difference. Right? So It's good. I told you guys I was a

648
00:40:01,445 --> 00:40:05,280
capitalist, through through. But it's not about

649
00:40:05,280 --> 00:40:08,960
making money. I've done a bunch of different things in my professional life and

650
00:40:08,960 --> 00:40:12,645
made money. Right? That I, Andy, when you're talking

651
00:40:12,645 --> 00:40:15,704
earlier, I don't get satisfaction fulfillment

652
00:40:16,325 --> 00:40:19,280
from my job, There's professional satisfaction

653
00:40:20,140 --> 00:40:23,680
that comes along with your work, and, ultimately,

654
00:40:24,620 --> 00:40:27,984
I get that satisfaction through making a difference. So I just feel like,

655
00:40:27,984 --> 00:40:31,744
man, I'm and and I guess I shouldn't be putting this out there, but

656
00:40:31,744 --> 00:40:35,530
at 57 years of age, I had no

657
00:40:35,530 --> 00:40:38,670
intention or no idea that I'd be working the kind of hours that I'm working,

658
00:40:39,130 --> 00:40:42,605
but I can't wait. I'm a real early riser, and it's because I can't wait,

659
00:40:42,605 --> 00:40:45,244
it's not because I'm stressed out, it's because I can't wait to get to work

660
00:40:45,244 --> 00:40:47,345
and make a difference. That's fantastic.

661
00:40:50,150 --> 00:40:53,510
Awesome. So we have, 3 complete the

662
00:40:53,510 --> 00:40:57,030
sentence, questions. When I'm not working, I

663
00:40:57,030 --> 00:41:00,454
enjoy blank. Interesting.

664
00:41:00,675 --> 00:41:04,454
Surfing. Cool. So you're on the East Coast you mentioned just a minute ago.

665
00:41:04,915 --> 00:41:08,510
Yeah. Where do you surf? So I have a place in

666
00:41:08,510 --> 00:41:12,050
Bethany Beach, Delaware, and so I'm storm chaser. I'm a hurricane chaser,

667
00:41:12,110 --> 00:41:15,865
but I will you know, I'll I'll find ways to get to places

668
00:41:16,005 --> 00:41:19,625
that have really good waves on a regular basis. Awesome.

669
00:41:20,244 --> 00:41:23,144
Awesome. Our next our next fill in the blank,

670
00:41:24,180 --> 00:41:27,700
is I think the coolest thing in technology today is

671
00:41:27,700 --> 00:41:31,220
blank. The pace of change. I think that,

672
00:41:31,220 --> 00:41:34,875
Andy, you guys were Frank both were talking about this

673
00:41:35,095 --> 00:41:37,974
earlier, and I think we were talking about it through the conversation, like, the differences

674
00:41:37,974 --> 00:41:41,275
that we've seen. And I think that, like, you you use the analogy

675
00:41:41,815 --> 00:41:45,220
of oil. We can also use the analogy of the

676
00:41:45,220 --> 00:41:48,520
industrial revolution and, like, how things change so rapidly.

677
00:41:48,980 --> 00:41:52,165
Everybody talks about the technology revolution. I mean, I think we should be talking about

678
00:41:52,165 --> 00:41:55,925
the data revolution, right, about what's going on and how fast it's

679
00:41:55,925 --> 00:41:58,505
all changing. So I I think that, ultimately,

680
00:42:00,390 --> 00:42:04,170
It's really cool. It's really cool no matter what frustrations I express,

681
00:42:04,309 --> 00:42:07,750
no matter what, you know, ripping off on

682
00:42:07,750 --> 00:42:11,575
about, you know, the fax machine. The pace of change is really cool right

683
00:42:11,575 --> 00:42:15,335
now, and I'm wired that way. Right? Like Nice. I think I love

684
00:42:15,335 --> 00:42:19,115
what I do every day because it's not predictable, and it changes

685
00:42:19,390 --> 00:42:23,230
Very rapidly. It does. Yeah. So, Frank, we need

686
00:42:23,230 --> 00:42:27,045
a new order for a t shirt. So, we've got Data is the

687
00:42:27,045 --> 00:42:30,724
new oil That's right. And which, listeners can pick up and

688
00:42:30,724 --> 00:42:34,085
help the show out. We need one for the, data

689
00:42:34,085 --> 00:42:37,830
revolution now. I think Joe inspired me. Yeah. I think, I think I'll

690
00:42:37,830 --> 00:42:41,610
be I'll be hitting up Photoshop later today. But

691
00:42:41,670 --> 00:42:44,935
the, Or maybe even DALL E. Who knows? I may I don't even have to

692
00:42:44,935 --> 00:42:48,775
do the actual artwork. So that's an interesting

693
00:42:48,775 --> 00:42:51,415
point because there was there was something you talk about data and and how it

694
00:42:51,415 --> 00:42:55,120
can transform it's Transform everything. Right? So one of the stories I heard was

695
00:42:55,240 --> 00:42:58,220
this is regards to staph infections, and I forget,

696
00:43:01,445 --> 00:43:03,865
speaking of rogue AI, Alexa, stop.

697
00:43:05,605 --> 00:43:09,400
She's like, She I don't know how what I did to trigger her, but

698
00:43:09,400 --> 00:43:12,680
the the short of it is is that there was a story about a, a

699
00:43:12,680 --> 00:43:16,265
woman lost her child Because he had some kind of staph infection

700
00:43:16,265 --> 00:43:19,944
or whatever, and she mentioned the notion of

701
00:43:19,944 --> 00:43:23,610
data leakage. Right? So it turns out, Again, I'm getting the details

702
00:43:23,610 --> 00:43:27,370
wrong. But, basically, if you track your heart rate or heart rate var

703
00:43:27,450 --> 00:43:31,095
variability, There's a signal in that

704
00:43:31,095 --> 00:43:34,075
in that data that there's some kind of massive infection,

705
00:43:34,855 --> 00:43:38,510
but most times, you know, when you're in the hospital, they only check your

706
00:43:38,510 --> 00:43:42,190
pulse but so often. So that I think that's a good example of how

707
00:43:42,190 --> 00:43:46,005
data can transform medicine. And is

708
00:43:46,005 --> 00:43:48,505
that is that something you've seen or heard of? Or

709
00:43:49,685 --> 00:43:53,365
or just getting the fundamentals right is just so is the process right

710
00:43:53,365 --> 00:43:57,200
now. So I think that, you know, I

711
00:43:57,200 --> 00:44:00,960
was involved a little bit more on the clinical side at different

712
00:44:00,960 --> 00:44:04,684
roles in pharma because But right now, I'm so focused

713
00:44:04,744 --> 00:44:08,285
on the administrative and logistical challenge

714
00:44:08,345 --> 00:44:11,980
associated associated with The

715
00:44:11,980 --> 00:44:15,200
care journey, I would say, I'm sure. There's example

716
00:44:15,580 --> 00:44:19,020
after example of that. Right? I think about some things that I've

717
00:44:19,020 --> 00:44:22,815
seen, right, in terms of Very similar things

718
00:44:23,115 --> 00:44:26,875
where, I just was exposed to something recently about

719
00:44:26,875 --> 00:44:30,349
suicides, and the The the the

720
00:44:30,410 --> 00:44:34,029
epidemic in the way certain groups are

721
00:44:34,170 --> 00:44:37,630
deciding to to to do this, and

722
00:44:38,175 --> 00:44:41,875
There's data in it. Right? Like, there's there's blood markers

723
00:44:42,015 --> 00:44:45,855
that are telling for okay. What happened? And what are

724
00:44:45,855 --> 00:44:48,990
we looking at? Why did that happen? That we can act more quickly.

725
00:44:49,210 --> 00:44:52,890
Right? So Yeah. To to help that that that

726
00:44:52,890 --> 00:44:56,724
person. So I think, absolutely, we probably could Bring somebody clinical

727
00:44:56,724 --> 00:44:58,965
line and talk to that all day long. But I can tell you the thing

728
00:44:58,965 --> 00:45:02,565
that I see that's really meaningful where and this will have an

729
00:45:02,565 --> 00:45:05,340
impact on health care. Right? The cell and gene Area

730
00:45:06,360 --> 00:45:10,200
is crazy with logistics. So these personalized immunotherapies, I don't know if

731
00:45:10,200 --> 00:45:13,955
you guys have heard much about that, where basically, You're

732
00:45:13,955 --> 00:45:17,495
having a patient go get blood drawn,

733
00:45:17,715 --> 00:45:20,775
have their t cells spud off, or sending it to a manufacturing

734
00:45:21,290 --> 00:45:24,970
Right. Then they're creating a personalized therapy for

735
00:45:24,970 --> 00:45:28,750
that patient, then it has to be shipped to a treatment

736
00:45:29,285 --> 00:45:33,045
which is not typically where the primary care is going on, and then that patient

737
00:45:33,045 --> 00:45:36,565
has to have things set up for the logistics around the 30 day inpatient

738
00:45:36,565 --> 00:45:40,010
stay, the care I just hit on a few things, but the

739
00:45:40,010 --> 00:45:43,230
logistical challenges in all of that, if you improve

740
00:45:43,930 --> 00:45:47,530
one thing that typically there's, like, 20 to 30 steps in

741
00:45:47,530 --> 00:45:51,195
that And they all take dates. If you can use technology to

742
00:45:51,195 --> 00:45:55,035
shrink those elements down, wow. These

743
00:45:55,035 --> 00:45:58,700
things are Curing. These personalized immunotherapies, we're not

744
00:45:58,700 --> 00:46:02,140
calling them cures yet, but I'm telling you, like, no evidence of disease. It looks

745
00:46:02,140 --> 00:46:05,485
like it's curing patients Where they were on 4th line of therapy, and they were

746
00:46:05,485 --> 00:46:09,085
gonna be dead in a month. It's having real impact, and

747
00:46:09,085 --> 00:46:12,050
technology Process improvement

748
00:46:12,670 --> 00:46:16,030
can really impact health care, and those are the things that we wanna study. When

749
00:46:16,030 --> 00:46:19,795
I think about data, like, I wanna be able to study that Time in motion,

750
00:46:19,795 --> 00:46:23,095
and how are we having an impact in the cell and gene space? I absolutely

751
00:46:23,234 --> 00:46:27,075
love that. Just just that the whole concept of reducing the

752
00:46:27,075 --> 00:46:30,890
steps. And if you can move you got somebody who's

753
00:46:31,030 --> 00:46:34,845
been diagnosed and and given a month to live. You're

754
00:46:35,005 --> 00:46:38,605
You're changing the you're moving the needle significantly if you

755
00:46:38,605 --> 00:46:42,205
can stop it from being Monday morning and make it Friday

756
00:46:42,205 --> 00:46:45,890
afternoon. I mean, that's significant, and

757
00:46:45,890 --> 00:46:49,590
that's amazing. It sounds like, oh, you're shuffling papers. Well,

758
00:46:50,210 --> 00:46:53,654
if you wanna look at it that way, knock yourself out. But I'll

759
00:46:53,654 --> 00:46:57,355
guarantee you, you know, that person is having that starfish

760
00:46:57,414 --> 00:47:01,200
experience where, you know, there's a kid on the beach picking up Starfish and

761
00:47:01,200 --> 00:47:04,960
throwing them back into the water, and somebody walks up and says, you know, what?

762
00:47:04,960 --> 00:47:08,640
You're this is useless. You're you know, they're just gonna wash back up

763
00:47:08,640 --> 00:47:12,285
again. Doesn't mean anything, and a kid says it means a lot to that

764
00:47:12,285 --> 00:47:15,885
one. He just chunked. For sure. Well,

765
00:47:15,885 --> 00:47:19,310
actually, getting back and think about getting back back getting back to Trife's point. Right.

766
00:47:19,310 --> 00:47:23,150
You think about these staph infections. Right? But let's think about it

767
00:47:23,150 --> 00:47:26,964
in the word way of, like, a blood infection, like sepsis. So,

768
00:47:26,964 --> 00:47:30,325
like, you get a blood infection, you're gone quickly. Right?

769
00:47:30,325 --> 00:47:34,000
So identification, diagnosis, Improvement

770
00:47:34,220 --> 00:47:37,900
in how things are being processed and identified through technology and

771
00:47:37,900 --> 00:47:41,155
through data will have a Critical

772
00:47:41,295 --> 00:47:44,975
impact on some of those things that are really truly urgent in the

773
00:47:44,975 --> 00:47:48,630
moment. I I love it. Wow. We went down a rabbit trigger here

774
00:47:48,630 --> 00:47:51,829
off our question. But that's that's what we do. I'm not even sure what question

775
00:47:51,829 --> 00:47:54,970
we are. I think we kinda It's u and number 6.

776
00:47:55,270 --> 00:47:58,915
Okay. I look forward to the day when I can use technology

777
00:47:59,135 --> 00:47:59,795
to blank.

778
00:48:02,575 --> 00:48:05,860
Totally off script, Brush my teeth and floss because that takes a lot of time

779
00:48:05,860 --> 00:48:09,460
for me throughout the day. But beyond that, I I actually am

780
00:48:09,460 --> 00:48:12,935
always, Jotting notes, and sometimes those

781
00:48:12,935 --> 00:48:16,775
notes become more formal documents and outlines and

782
00:48:16,775 --> 00:48:20,530
wireframes for The the next thing. Right?

783
00:48:21,010 --> 00:48:24,630
I'm still a very young 57. I tell my kids I'm biologically

784
00:48:24,770 --> 00:48:28,395
31. So I got 2 or 3 or more gigs in me.

785
00:48:28,455 --> 00:48:31,994
And so I look forward to using technology

786
00:48:32,135 --> 00:48:35,494
to solve for food deserts. I look forward to using

787
00:48:35,494 --> 00:48:38,460
technology to solve for a comprehensive

788
00:48:39,240 --> 00:48:43,000
approach approach to health care insurance alternatives. I think our

789
00:48:43,000 --> 00:48:46,380
insurance industry our health care insurance industry is totally broken.

790
00:48:46,575 --> 00:48:49,935
Right? The way we approach it, the way we just approach

791
00:48:49,935 --> 00:48:53,375
insurance, right, through the standard brokerage approach, through the standard

792
00:48:53,375 --> 00:48:56,030
markets approach, I think we need to open that up, and I think we can

793
00:48:56,030 --> 00:48:59,410
open up through SMEs. Right? So subject matter

794
00:49:00,190 --> 00:49:03,250
experts applied to technology. So those are the things

795
00:49:03,725 --> 00:49:07,425
Besides flossing and brushing my teeth, I would love to apply type 2.

796
00:49:07,805 --> 00:49:11,585
That's funny you made that because there was a TV show called Farscape,

797
00:49:11,990 --> 00:49:15,830
Like, in the early early 2000. And and one of the one of the plot

798
00:49:15,830 --> 00:49:19,610
points I forgot was, like, they they they have, like, little nanite,

799
00:49:21,235 --> 00:49:24,995
robots that clean and floss your teeth for you. Forget that that came up. Yeah.

800
00:49:24,995 --> 00:49:26,855
Yeah. Yeah. That's a great idea.

801
00:49:28,595 --> 00:49:32,339
So our next, thought is we asked to asked that you

802
00:49:32,339 --> 00:49:35,780
share something different about yourself. You already mentioned surfing.

803
00:49:35,780 --> 00:49:39,605
So, but it, and we remind all our

804
00:49:39,605 --> 00:49:43,444
guests, not just you, to, remember it's a family podcast, and

805
00:49:43,444 --> 00:49:45,625
we wanna hang on to our clean rating.

806
00:49:47,910 --> 00:49:51,590
I think that I don't

807
00:49:51,590 --> 00:49:55,050
know how different this is, but I think my obsession

808
00:49:56,015 --> 00:49:59,455
Is what makes it different? I am obsessed with challenging the

809
00:49:59,455 --> 00:50:02,195
norm and expected behavior.

810
00:50:03,070 --> 00:50:06,830
I think that I think that too

811
00:50:06,830 --> 00:50:09,010
many of us in this world

812
00:50:10,724 --> 00:50:14,405
I've settled in the fact that this is what

813
00:50:14,405 --> 00:50:18,250
it's expected. This is what is the norm. This is what

814
00:50:18,250 --> 00:50:22,090
we're told to do, and this is how we're told to do it. Yeah. I

815
00:50:22,090 --> 00:50:25,924
think that there are more people That it could have

816
00:50:25,924 --> 00:50:29,684
a major impact on others if they would in the right way.

817
00:50:29,684 --> 00:50:33,365
Right? This is family friendly. I'm not talking to do things to to do

818
00:50:33,365 --> 00:50:36,950
things that are Harmful or nefarious around challenging norms

819
00:50:37,009 --> 00:50:40,849
and expected behaviors, but I think in a healthy way, my

820
00:50:40,849 --> 00:50:44,465
obsession with it is sometimes unhealthy, but I think that's a little different

821
00:50:44,465 --> 00:50:47,845
and maybe even a little off with me. Interesting.

822
00:50:48,145 --> 00:50:51,605
You know, one of, Frank and I, follow,

823
00:50:52,940 --> 00:50:56,540
success coaches, and and the one I'm thinking of, I know it went through

824
00:50:56,540 --> 00:51:00,300
Frank's mouth whenever we hear the word obsess, is, Grant

825
00:51:00,300 --> 00:51:03,845
Cardone. And he has a book called Be Obsessed or Be Average.

826
00:51:04,305 --> 00:51:08,145
And it's, it was a very challenging, listen for, for

827
00:51:08,145 --> 00:51:11,550
me. I know Frank Got a lot out of it as well. And,

828
00:51:12,490 --> 00:51:16,010
I I don't think there's anything wrong with being obsessed. I I think,

829
00:51:16,010 --> 00:51:19,825
yes, Like anything, it can be taken to an unhealthy

830
00:51:19,964 --> 00:51:23,724
spot. But it I don't think that that spot out you know,

831
00:51:23,724 --> 00:51:27,359
the word that popped into my mind first was extreme. But I'm not so

832
00:51:27,359 --> 00:51:31,200
sure extreme is the definition of unhealthy there. I think there

833
00:51:31,200 --> 00:51:34,994
are 2 different spots and I can hear the passion, in your

834
00:51:34,994 --> 00:51:38,355
voice, Joe. And for those listening to the audio, you can't see Joe's

835
00:51:38,355 --> 00:51:42,160
face. Here we are recording this as video, but then we usually Strip

836
00:51:42,160 --> 00:51:45,520
out the audio because we got more listeners, listening than

837
00:51:45,520 --> 00:51:49,359
watching. But I I get it. I can see

838
00:51:49,359 --> 00:51:53,075
that in your it's a good passion. It's not unhealthy at

839
00:51:53,075 --> 00:51:54,215
all in my opinion.

840
00:51:56,995 --> 00:52:00,595
Interesting. Okay, Frank. I lost track now. No. No. No. No. I think it's you.

841
00:52:00,595 --> 00:52:04,430
I think you're not And I'm going to do the audible one, then we'll ask,

842
00:52:04,670 --> 00:52:07,950
the final question, which is where people can find out more. So do you do

843
00:52:07,950 --> 00:52:11,635
you do audio books or or or, Or not. If you do, can you

844
00:52:11,635 --> 00:52:15,475
recommend a book? So I I typically don't do audio.

845
00:52:15,475 --> 00:52:19,070
I love to read, because I love words, But I

846
00:52:19,070 --> 00:52:22,770
typically love to read to take me away,

847
00:52:23,550 --> 00:52:26,910
because of my obsessed approach to things. I'm always

848
00:52:26,910 --> 00:52:30,385
on. So I have one that I can

849
00:52:30,385 --> 00:52:33,185
recommend that I read years years ago, and I still think it was one of

850
00:52:33,185 --> 00:52:36,950
the the the the more Thought provoking take

851
00:52:36,950 --> 00:52:40,410
me away books that I read, but I I do need to say first

852
00:52:40,470 --> 00:52:44,095
that the book I recommend to everyone is the bible. So that's the

853
00:52:44,095 --> 00:52:47,934
one that that I actually read every day and

854
00:52:47,934 --> 00:52:51,234
I find important as it relates to guidance, advice,

855
00:52:51,980 --> 00:52:55,819
And how I should be approaching things from a foundational perspective, but it

856
00:52:55,819 --> 00:52:59,485
also reminds me daily of my failures in trying to accomplish that.

857
00:53:00,205 --> 00:53:03,105
The other the book that I I would recommend,

858
00:53:04,125 --> 00:53:06,785
is The Genesis Code by a pseudonym

859
00:53:07,970 --> 00:53:11,730
John Case. And it's a biological thriller. So thought

860
00:53:11,730 --> 00:53:15,570
provoking, some cutting edge, interesting blending of that could

861
00:53:15,570 --> 00:53:19,085
be possible, Not possible type type stuff. Interesting.

862
00:53:20,345 --> 00:53:24,045
Awesome. Audible is a sponsor of Data Driven. If you go to the datadrivenbook.com,

863
00:53:25,890 --> 00:53:29,650
I'm sure I know that there's multiple readings of the bible. My

864
00:53:29,650 --> 00:53:33,490
my, my wife uses Audible all the time because I see it come through. There

865
00:53:33,490 --> 00:53:37,305
you go. Oh, very cool. Yeah. I I am the

866
00:53:37,305 --> 00:53:41,005
heavy user. I I use my wife's credits because we're on the same account. So,

867
00:53:44,660 --> 00:53:48,180
I don't know if that's that ranks in the share Netflix password thing, and somebody

868
00:53:48,180 --> 00:53:51,860
from Audible's gonna give me a call. I don't know, but we'll find out.

869
00:53:51,860 --> 00:53:55,365
That's what data science is all about. I have Hypothesis, I do it.

870
00:53:55,365 --> 00:53:58,965
Mhmm. Sometimes I plan the hypothesis. Sometimes it just comes out of my

871
00:53:58,965 --> 00:54:02,645
mouth. So on that note, before I say anything

872
00:54:02,645 --> 00:54:06,450
else stupid, Share something. No. We did that. Where

873
00:54:06,450 --> 00:54:09,970
can people find out more about you and your company? The best place to go

874
00:54:09,970 --> 00:54:13,349
is our website, anexus, a n n e x

875
00:54:13,475 --> 00:54:16,695
US. And by the way, a Nexus is

876
00:54:16,835 --> 00:54:20,675
for connectivity in Latin, and you guys heard through the stories

877
00:54:20,675 --> 00:54:23,960
today Yeah. That connectivity is really to what we do, so ennexushealth.com.

878
00:54:26,500 --> 00:54:29,940
Okay. Excellent. Well, thank you, and we'll let Bailey finish the

879
00:54:29,940 --> 00:54:33,105
show. Thanks for listening to Data Driven.

880
00:54:34,045 --> 00:54:37,805
If you have any suggestions for future episodes or topics you'd like us

881
00:54:37,805 --> 00:54:41,529
to explore, please reach out to us. We value

882
00:54:41,529 --> 00:54:44,890
your feedback and strive to address the needs and interests of our

883
00:54:44,890 --> 00:54:45,390
listeners.