Episode #153. How Tiny Workflow Tweaks Can Reduce Massive Physician Burdens, with Jason Hill & David Leingang

Summary

Podcast Summary: Risk Never Sleeps Podcast

Episode #153: How Tiny Workflow Tweaks Can Reduce Massive Physician Burdens, with Jason Hill & David Leingang
Host: Ed Gaudet
Guests: Dr. Jason Hill (Innovation Officer, Ochsner Health), David Leingang (Director of Innovation Data Science, Ochsner Health)
Date: December 8, 2025

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Episode Overview

This episode explores how targeted workflow adjustments and data-driven innovation at Ochsner Health are dramatically reducing the burden on physicians—particularly the avalanche of patient messages (the “in-basket” problem). Dr. Jason Hill and David Leingang both discuss the intersection of technology (especially AI/ML) and practical changes, the process of transforming data into operational improvements, and why many healthcare improvements don’t require complex tech—just thoughtful process changes.
The conversation also touches on the cultural and organizational challenges of digital transformation, the importance of problem scoping before deploying AI, and the power of collaboration between academic, healthcare, and industry partners.

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Key Discussion Points & Insights

1. The Scale of the Physician Message Burden

• Ochsner Health receives roughly 2.4 million messages annually, a significant portion of which reaches providers unnecessarily.
• GLP1 (Ozempic and related weight loss medications) queries made up about 4% of all patient messages—thousands each year.

  "About 4% of all our patient messages had something to do with a weight loss medication." – David Leingang [01:43]

2. How Ochsner Approached the Problem

• Data-driven Triage: Started with a comprehensive analysis of all system-wide messages, building models to discover trends and frequent topics.
• Developed a machine learning approach to route messages more intelligently (though not fully deployed—waiting to see how Epic, the EHR provider, advances similar functionality).
• Operational Response: Insights from data led to operational changes—resulting in a new digital medicine, weight management program to support appropriate GLP1 prescribing.

  "We actually created a weight management program that was part of our digital medicine offerings at Ochsner." – Dr. Jason Hill [03:32]

3. Do You Need AI? Sometimes, No.

• Workflow before AI: The team stresses the value of basic workflow changes and provider education before turning to resource-heavy AI projects.

  "Do we really need to build a model...or do we just do a little education and then our physicians take over and we've accomplished the same thing without spending that time and money?" – David Leingang [04:18]

• Simple Tweaks with Big Impact: For example, Ochsner reduced provider message overload by changing the order of message options in their portal:
  • Moved “send a message to my doctor” to the bottom.
  • At the top: “I have a new problem that needs medical attention”—which triggers an eVisit, capturing detailed info upfront.

  "We actually reduced our click share by about 10% by just changing the order of the messages." – Dr. Jason Hill [05:24]

4. Measuring the Right Metric

• Key realization: Time spent in the in-basket isn’t the right metric; fewer received messages is much more important.

  "The best message I receive is the message I don’t receive." – Dr. Jason Hill [04:39]

5. Workflow & Technology: How They Interact

• The right workflow changes can make complex technologies work better—or even unnecessary.
• Real-world best practices are being shared, sometimes leading EHR vendors (like Epic) to adopt the innovations.

6. Root Cause Analysis and Model Iterations

• ML excels at finding associations, not necessarily causality. It narrows down problems for human experts to analyze.

  "ML can work really good at figuring out what's the root cause. ...It gives you...20 [possibilities] instead of an infinite canvas..." – Dr. Jason Hill [07:02]

• Ochsner’s Deterioration Index: Predicts which hospitalized patients may worsen soon. The team adjusted the prediction time window after finding earlier predictions didn’t prompt action.

  "If you actually tuned up your algorithm a little bit more and made it a little closer to the event, the patients would look sicker and people would actually do something about it." – Dr. Jason Hill [08:13]

• As predictive interventions improve patient outcomes, the model’s predictive power naturally declines, requiring retraining.

7. Value-Based Care and Data

• Value-based care changes both incentives and what data is valuable; to maximize positive patient outcomes, risk contracts need to match the patient base.

  "If you don't actually expand your risk contracts to include more of your patients, then you're taking value based actions without the value based reimbursement." – David Leingang [09:15]

8. The Power of Collaboration: The AHEAD Network

• Ochsner is launching a collaboration (AHEAD Network) with universities across the Gulf South to connect PhD/postdoc data scientists hungry for real-life healthcare problems with hospital and industry partners.

  "If we can bring the rural hospitals and bring those networks together, give them the problems, then all of a sudden it becomes a very virtuous relationship." – Dr. Jason Hill [11:49]

• Easier tools and AI are democratizing innovation—now, small teams (even at rural hospitals) can implement improvements rapidly.

  "You don’t need a giant team. You can have two or three AI engineers...moving things at an unbelievable clip..." – Dr. Jason Hill [11:21]

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Notable Quotes & Memorable Moments

• On the Unintended Positive Impacts of Data:

  "Because of that data, we actually created a weight management program..." – Dr. Jason Hill [03:32]

• On AI as a Tool, Not a Solution:

  "People think of AI as this big, nebulous thing, but truly it's just a tool to solve problems." – Dr. Jason Hill [03:55]

• On the Best Message Metric:

  "The best message I receive is the message I don't receive." – Dr. Jason Hill [04:39]

• On Model Implementation:

  "We became a victim of our own success and our model became less predictive over time." – Dr. Jason Hill [08:43]

• On Working in Data Science in Healthcare:

  "It's sometimes hard to recognize that direct impact. But in healthcare it's there; it's extremely rewarding." – David Leingang [15:55]

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Lightning Round & Closing (Personal Touches)

• Why Did You Get into Healthcare?

  • David Leingang: “Started in audit, stayed for the value—helping people.” [15:01]
  • Dr. Jason Hill: “Life event—car accident made me reconsider career; caring for people became a calling.” [16:25]

• Advice for Your 20-Year-Old Self:

  • “Take more math classes.” – David Leingang [17:36]
  • “You’re not going to believe it, but someday you’ll end up a doctor.” – Dr. Jason Hill [17:54]

• Desert Island Records:

  • David: Garth Brooks’ Seven, Eve 6 Inside Out, Mighty Ducks movie [19:20]
  • Jason: Red Hot Chili Peppers Blood Sugar Sex Magik, Pearl Jam Ten, The Matrix Trilogy [20:03]

• Riskiest Thing Ever Done:

  • David: Skydiving (twice solo!) [21:43]
  • Jason: Race-driving Ferraris and Lamborghinis in Vegas [22:38]

• Funnest Weapon Fired:

  • Jason: Japanese anti-aircraft rifle; “almost blew my shoulder off.” [24:02]

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Timestamps of Key Segments

• Introduction & Background: [00:05]–[01:09]
• Ochsner’s Messaging Burden & Data Insights: [01:09]–[03:52]
• AI as a Tool vs. Education/Workflow: [03:55]–[04:39]
• Workflow Tweaks for In-basket Management: [05:11]–[06:33]
• On Metrics, Anxiety, & Best Practice: [05:11]–[06:39]
• Root-Cause Discovery via ML: [07:02]–[08:50]
• Adjusting the Deterioration Index: [08:03]–[08:50]
• Value-Based Care & Data Science: [09:15]–[09:53]
• AHEAD Network / Collaboration: [11:49]–[14:24]
• Personal & Lightning Rounds: [15:01]–[23:40]
• Closing Contact Info: [26:00]–[26:16]

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Tone & Style

Conversational, humorous, pragmatic, and motivational. Both guests are candid and practical, focused on solving real-world clinician problems, and enthusiastic about the democratization and acceleration of healthcare innovation. The host’s tone is engaging, blending professional respect with friendliness.

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Further Information

• To contact Dr. Jason Hill: [e-mail]
• To contact David Leingang: [e-mail]
• More on patient safety & risk: Censinet – Show Notes & Resources

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Summary prepared for listeners seeking actionable insights on healthcare workflow innovation and the practical application of data science and AI.

Transcript

[Ed Gaudet]

Foreign.

Welcome to Risk Never Sleeps, where we meet and get to know the people delivering patient care and protecting patient safety. I'm your host, Ed Gaudet.

[Saul]

Hello, everybody. Welcome to the AI Med Insights podcast. I'm so excited to be hosting two outstanding guests with us, but of course, I want to first say hello to my co host, Ed.

[Ed Gaudet]

Hey, Saul. Haven't seen you in a while.

[Saul]

I know. We're making a habit of this thing, aren't we? It's crazy.

[Ed Gaudet]

Well, we have to move out here.

[Saul]

San Diego's a good place. We have two outstanding guests, as I was sharing today. First, I want to introduce you to Dr. Jason Hill, innovation Officer at Ochsner Health. And of course, we also have the outstanding David Leingang, who is a director of Innovation Data Science at Ochsner Health. Welcome to the podcast, gentlemen.

[David Leingang]

Thanks for having us.

[Ed Gaudet]

Yeah, welcome. You just gave a riveting presentation that I went to on how you're attacking the whole message problem for physicians.

[Dr. Jason Hill]

Right.

[Ed Gaudet]

Their inbox, the stats you showed using GLP1, I think.

[David Leingang]

Yeah, that was just the use case we looked at. But, you know, there's a lot of work being done to really streamline in basket messaging to get them to the right person. You know, it doesn't always have to be a provider that's looking at those. So how do we reduce that cognitive burden and that load on our physicians and still apply the right clinical care for our patients?

[Ed Gaudet]

And how did you do it?

[David Leingang]

Like, technically. Yeah, I should have brought Leo in for this one. No.

[Ed Gaudet]

So at a high level, but just technically, it's interesting, like, what did you.

[David Leingang]

Have to do to make it happen? Built a model that looked at all of the messages in the entire system, so 2.4 million a year or so, and really analyzed the frequency of the words that were coming in. And so built a word cloud looking at what was showing up the most. And it was about 4% of all of our patient messages had something to do with a weight loss medication. So Ozempic, weight loss, drug shot, you know, something along those lines. And then we looked at could we use machine learning to route those messages to the right place? One thing that we didn't say in there is we didn't actually put this into production. You're testing it, we're reevaluating whether it makes sense for us to do it internally. Okay. Epic's doing a lot of work on some in basket routing. And so.

[Ed Gaudet]

So you gave EPIC the idea and they're going to build it into the.

[Dr. Jason Hill]

AS would typically work, which Is good. That's. But the important thing is, thinking about it this way. The. There are a lot of data in healthcare, and a lot of people view it as a lot of garbage. Right? There's a lot of garbage data. But with the techniques that we learn now, newer machine learning techniques give the capability to unlock a lot of that data. What David also is not saying is that that data actually convinced us to do a weight management digital medicine program. Because those of you who remember just before recently, when Trump just signed that GLPs will be like 150 bucks, which is amazing. But they were a lot more.

[Ed Gaudet]

Yes. 1200.

[Dr. Jason Hill]

$1200. Which meant that if you're thinking 4% of all messages were concerning GOPS, there's $1,200 per drug, per month, per patient, and the success rate for GLPs goes down dramatically. If you're not already enrolled in a weight management program, it behooves us to make sure that everyone we're giving a GLP to is already enrolled in a weight management program. So because of that data, we actually created a weight management program that was part of our digital medicine offerings at Ochsner, so that whenever a patient needed it for weight management, and they were on our plans, that really required that to happen, that we had something available.

[Ed Gaudet]

For them, positive, unintended consequences.

[Dr. Jason Hill]

Well, this is the thing. Like, you could, you know, when you look at AI, right? Like, people think of AI as, like, this big, nebulous thing, but truly it's just a tool to solve problems. It's like another tool in our bucket that we can use to solve problems. And so one of the things that David and I do as part of our daily job is try to figure out, is this problem worth solving and can AI actually fix it in some way, shape, or form?

[David Leingang]

Can we solve it through a workflow change or just some education? Do we really need to build a model and spend the time, the resources, the consumption costs, all of the other things that go along with that, or do we just do a little education and then our physicians take over and we've accomplished the same thing without spending that time and money? Yeah.

[Dr. Jason Hill]

Important thing about in basket. So to actually decrease the in basket messages we got at Ochsner, one, we had to realize that our metric was wrong. We said, well, if we just spend less time in basket, that'll make everybody happier, right? No, not true. Because what the best message is, our primary care lead would tell me, the best message I receive is the message I don't receive, which mean you can't you end up with a divide by zero error if you're looking at. Right. So you're actually decreasing the amount of messages that get to the provider. If you. So you're actually answering the wrong problem. If you think of it that way.

[Ed Gaudet]

I imagine there'd be some anxiety too, that this could produce for the clinician. Right. Am I getting everything that I need to get? I'm missing things through this process. Tell us about that.

[Dr. Jason Hill]

Yeah, so there's a whole system we set up, and it's a bunch of layers. I actually just gave this talk last week in D.C. so you can think of it this way. You know, as David had said, you don't need AI to solve every problem. The first thing we found, we actually reduced our click share by about 10% by just changing the order of the messages. In my turn, we basically took the message and said, I want to send a message to my doctor. We put it at the very bottom, and at the very top we said, I have a new problem that needs medical attention. And what we did was when you clicked into that problem, it actually take. You. Took you to what's called an E visit, which is a way to actually lingually, like, lay out your whole problem. So what you're buying happen is like Saul's pretending. Saul's my patient. He'll be like, hey, Doc, I have this, like, itch. And I'll be like, okay, where is it? It's always itch, Saul. It's like on my neck and it's been going on for a week. And then I send a message back and, you know, it's like that back and forth. That just chews up your time.

[David Leingang]

Right.

[Dr. Jason Hill]

So in evis, it allows you to kind of lay out that whole case and then a doctor can pick them.

[David Leingang]

That seems like paid for it and they get paid.

[Ed Gaudet]

That seems like a best practice for epic. Are they going to.

[Dr. Jason Hill]

Doesn't it?

[Ed Gaudet]

Yeah.

[Dr. Jason Hill]

Yeah.

[Ed Gaudet]

You think they're going to do that as a. Oh, they are.

[Dr. Jason Hill]

Okay.

[David Leingang]

They are.

[Dr. Jason Hill]

We led the nation in E visits for the past couple years, but it seems like epic's finally picking that up and starting sound.

[Ed Gaudet]

Breaking news here on the insights podcast that AI Med 25.

[Dr. Jason Hill]

That's right.

[Saul]

Well, you know, it just points to like this whole example that you guys bring up, points to the importance of being thoughtful about your approach and not just throwing technology at things and using the history that you have and how to care for patients and then layering technology on top of that.

[Dr. Jason Hill]

Yeah, it can work. And ML can work in any Areas of that stack. ML can work really good at figuring out what's the root cause. Right. So you can then look at associations. Right. ML is really good at finding associations, may not be good with particularly causality, but you can look at those. It then gives you going from an infinite canvas of potential root causes to like 20. And then you get a bunch of smart people in the room together and figure out are those things actually associated? Right. So you can look at it at that on the front end. You can then look at it on the back end as well. Are you doing something? Does that then allow. Does that thing that you are doing actually decrease some of the probability stuff that you're working in? A good example, that's our deterioration index that we do. Right. And so we actually kind of became a victim of our own success with the deterioration index. So we made this whole index that we figure out if a patient is going to likely deteriorate and get worse in the hospital. Two important parts we found out when we first designed it. We designed it for like six to eight hours out and realized that those patients actually weren't sick enough for people to do anything about. So they would go in, they would look at them and be like, this person's not sick.

[Ed Gaudet]

Happens all the time. Yeah.

[Dr. Jason Hill]

And so we send them home, and.

[Ed Gaudet]

Then they get worse.

[Dr. Jason Hill]

And we didn't send them home.

[Ed Gaudet]

But other places might.

[Dr. Jason Hill]

But like, what we found is if you actually tuned up your algorithm a little bit more and made it a little closer to the event, the patients would look sicker and people would actually do something about it. So it's like, you know, you'd always need something that looks good for the numbers. You have to think about what the. What your action that the individual is going to take would you predict as well?

[Ed Gaudet]

Yeah, we're pretty good at it.

[Dr. Jason Hill]

And then we ended up getting so good at it that our population changed because we started predicting it. Right. And then a bunch of those people that were going to deteriorate all of a sudden didn't deteriorate because we made them better.

[Ed Gaudet]

Yes.

[Dr. Jason Hill]

And then we became a victim of our own success and our model became less predictive over time, which we're had. We just recently retrained it.

[Saul]

Yeah.

[Dr. Jason Hill]

Xd.

[Ed Gaudet]

Are you selling health supplements to make people well?

[Dr. Jason Hill]

Sorry, isn't that's. That's CM. That's CMMI's job now, not us.

[Ed Gaudet]

That's a joke. That fell flat, folks. You should have seen their faces. I wish we had this on video.

[Saul]

Well, you know, and I Guess if you think about this from a risk perspective, like value based arrangements, this could become very interesting. Right? You want to speak to that or.

[David Leingang]

Yeah, I mean, so before moving into this innovation role, I was actually the director for analytics for our value based care team. And the impact that the data makes and the impact that value based care can have to our patients is just, it's so enormous. And I think if we don't expand that because the finances are totally different. And so if you don't actually expand your risk contracts to include more of your patients, then you're taking value based actions without the value based reimbursement. And that gets really interesting on the hospital side. So, you know, Ochsner is doing a lot to really grow our value based population.

[Saul]

That's awesome, man. Congrats. Congrats on that. What brings you guys to this meeting? I mean, you guys are top of the game here in AI. What brings you to Aimed?

[David Leingang]

I think for me it's who's a little bit ahead of us, who has kind of gone through what we're going through and shown tangible outcomes and success. You know, it's one thing to build a strategy and a framework and we're kind of building it all while doing it all at the same time. But who's getting those actual outcomes and what have they learned that we can learn from, not stumble through in the next?

[Ed Gaudet]

Who comes to mind when you think about that?

[David Leingang]

That's a great question, actually.

[Ed Gaudet]

Thank you. Yeah, could be Novari from a bad joke. Yeah, there we go. A great question.

[Dr. Jason Hill]

I mean, we're early in the conference, so I don't wanna, I don't wanna spoil anything for anyone. I think that a lot of the work that' of some of the work around that Mayo has done for chart search, number one, for me, that work is really cool. I think that work is ultimately something that EPIC will do and I think that ultimate. But it tells us, okay, what are the capabilities that you need for a team? Our team is small but mighty. So we don't have a 200 data scientist team. So we need to be able to think of something that other folks are doing that you can do with a small team. And that's one of actually the neat things about AI is that actually don't need a giant team. You can have two or three AI engineers that are cranking out tools and moving things at an unbelievable clip that.

[David Leingang]

Used to take years are now taking days.

[Dr. Jason Hill]

Amen.

[Ed Gaudet]

That's the message for rural hospitals too. Like, there's so much you can do now that you couldn't do even a year ago with AI?

[Dr. Jason Hill]

Well, I think the, you know, the first model and last mile are going to be the hardest parts there. So like the first mile is like determining what problems you need to solve and iterating on that. And the last mile is like developing that workflow integration and getting people over it. And I think that's.

[Ed Gaudet]

And finding money to support it, which is hard.

[Dr. Jason Hill]

Money's always hard in healthcare. But in, but so if you'll attend my talk as a shameless plug about the AHEAD network, we're actually creating a network with a bunch of universities across the Gulf south and with Emory, Georgia Tech, University of Florida, Tulane, University of Louisiana. Ochsner is one of the founding members of that that is actually leveraging university resources of data science. These are hungry PhDs and postdocs that want to do work in healthcare, but they don't know the problems. Right. So if we can bring the rural hospitals and bring those networks together, give them the problems, then all of a sudden it becomes a very virtuous relationship. Right. Like they can actually solve a lot of get use the data science resources.

[Ed Gaudet]

Give us a preview of your talk. Tell us a little more about the association.

[Dr. Jason Hill]

Yeah, so the Ahead network is something that we've been working on. I've been pushing that Bulldog Hill for a couple years now. And it's really been a labor of trying to get together all these three different groups of people that need are interpendant on each other. One being industry that has the money capital. Right. The other being healthcare that has the problems and the third being universities that have potentially the people that can help solve problems. Right. And so, you know, there's a lot of issues and getting those three large groups of people with very different strategies together.

Exactly. And together. But like I think now that we have moved this down two years, like if you were to look at this two years ago and say, well, is this even possible? I'll be like 30, 70. And it actually we just. I was in Atlanta last week at the kickoff meeting at Emory. Yeah. Oh, actually it was at Georgia Tech. Oh, Georgia Tech in Georgia Tech. But Emory was there and so there. And so like the whole concept that people are now realizing is that you don't need a company to solve a lot of these problems. And I think that people also don't realize as well that the solutions are way easier to design, which is why we're seeing this big flood, which is why health is just insane now. Right. Because the solutions are easy, which means that there's a lot of people, if you put a little capital behind them.

[Ed Gaudet]

That can design AI, accelerating partnerships and integrations and collaborations. I love that. Yeah, that's an emerging theme.

[Dr. Jason Hill]

No one's really talking about that. It's unique. The National Science foundation are actually going to get funded hopefully by the nsf. If there is an NSF next couple of years.

[Ed Gaudet]

No followings on this Joe Fair. You think it's all crazy, you'll start throwing things fair.

[Dr. Jason Hill]

So yeah, we're hopefully getting it funded by the NSF and then that'll represent some really good collaborations with our own, with David's team because he knows a lot of those team too from his work.

[David Leingang]

We had a kind of a pre symposium or pre kickoff symposium this summer. We had decent attendance. I think there was probably about 50 or 60 people there, but it was all people in the area. And really interesting work, really great collaboration. I think there's going to be so much value there.

[Saul]

Nice.

[Ed Gaudet]

All right, let's do the shotgun round. Right?

[David Leingang]

We saw the shotgun round. That's all right.

[Saul]

Yeah.

[Dr. Jason Hill]

So from Louisiana, we were all about that.

[Ed Gaudet]

That seems weird for some reason. I don't know why you ever do that. When you say a word you're like, that's not the right word.

[Dr. Jason Hill]

All the time. All the time.

[David Leingang]

All right.

[Ed Gaudet]

Why did you get into healthcare, David?

[David Leingang]

Two answers. One kind of fell into it came from an audit world where most of the data was in was healthcare data. So auditing state with state auditor. A lot of work with Medicaid, but stayed in it really because I saw the value in helping people.

[Ed Gaudet]

Nice.

[David Leingang]

And actually bring this up with our team a lot. And it was a lot harder on the value based care population health side because we were four steps removed from a patient, if not more with this innovation team like Jason talked about that deterioration model and we had an email come through last week. We actually went live at a new site, a new Ochsner site with our model and it was the chief nursing officer saying we had our first patient transfer to the ICU from the model. And so you could really feel the actual impact. Saving a life. It really means something.

[Ed Gaudet]

Yeah.

[David Leingang]

And in kind of a data science world sometimes that's hard to recognize that direct impact. But in healthcare it's there and you know, it's, it's extremely rewarding.

[Ed Gaudet]

Nice. We're gonna modify the name of this. It's gonna be called the lightning round now.

[Saul]

It came to me, I was like, it's lightning.

[Ed Gaudet]

Hey, shotguns.

[Saul]

Lightning.

[Ed Gaudet]

It's a shotgun Wedding. I mean, I don't know what the. I didn't eat. I haven't eaten yet, so that could be they ran out of food. Same question, Jason.

[Dr. Jason Hill]

I had kind of a life event happen to me when I was in college and it was a car accident and had a lot of interaction with the healthcare system, both good and horribly awful. And it made me think about my direction. You know, I was actually, my undergraduate degree was in engineering and I was moving rapidly towards a graduate degree in engineering when all, you know, and then realized, sort of like in that process that I really wanted to take care of people. And I think that's probably the calling of healthcare, is that you feel shared mission. Yeah, well, it's a calling. You feel.

[Ed Gaudet]

It is a call.

[Dr. Jason Hill]

You really do.

[Ed Gaudet]

They don't pay us enough, right? So it's gotta be something.

[Dr. Jason Hill]

I mean, we get paid. Okay. I'm not gonna say are not good.

[Saul]

David's gone.

[David Leingang]

I don't get.

[Ed Gaudet]

I don't think I need my move. David's like, I don't get paid.

[Saul]

Okay, I gotta task out for this.

[Dr. Jason Hill]

And so he's a data scientist. They're hiring these guys like professional athletes. Okay, so like, how you doing?

[Ed Gaudet]

Oh, you're a data scientist.

[Dr. Jason Hill]

Oh my.

[Ed Gaudet]

Please.

[Saul]

Yeah, you give an autograph, you know.

[Dr. Jason Hill]

$100 million comp package from Meta is on his way. Don't worry.

[Ed Gaudet]

Far cry for David, Argentina. Okay. All right, next question. Go back in time, see your 20 year old self. What would you tell him?

[David Leingang]

Take more math classes. Math.

[Ed Gaudet]

Oh, nice.

[David Leingang]

I didn't really get into it until grad school and it would have made grad school a whole lot easier.

[Ed Gaudet]

Nice. Differential calculus and all that jazz. Nice. All right, Jason, what would I have.

[Dr. Jason Hill]

Told my 20 year old self? You're not going to believe it, but someday you'll end up a doctor.

I think my 20 year old self did not believe me. Well, I was like, I told them.

[Ed Gaudet]

Oh, okay.

[David Leingang]

Yeah.

[Ed Gaudet]

Isn't it crazy how the universe just sort of takes you on this amazing journey?

[Dr. Jason Hill]

If you let it, it really is. You wake up and you're a doctor.

[Ed Gaudet]

Not just a doctor though.

[Dr. Jason Hill]

The weird thing is you end up a doctor and then you end up a clinical informaticist and then you end up being the clinical partner to a data science team and you're like, what? Yeah, how did I plot that trajectory?

[Saul]

And that's the really cool thing about it, right?

[Ed Gaudet]

So you have kids, like five of them. What happened?

[Saul]

Yeah, and Jason, you've done like, you know, I've Had a chance of chatting with you before and, you know, from this scribe AI scribing, which is what we were talking about last time, to the work that you're doing now in predictive medicine, like, your background in engineering is what actually carved your space and what you're doing now as a physician. So that's pretty cool.

[Dr. Jason Hill]

Yeah.

[Ed Gaudet]

Yeah, very cool. All right. Desert Island. Five records albums. What would you pick? You could do movies, too, if you're more of a movie guy than a music guy, but. Oh, man. Little Lincoln Parker. Do like some Lincoln Park.

[Saul]

Little sound garden.

[Ed Gaudet]

I can see you. I see you, man.

[David Leingang]

I feel you. Do some karaoke on stage Last Week with Eve 6.

[Ed Gaudet]

Oh, that was a good thing.

[David Leingang]

I know, huh?

[Ed Gaudet]

Yeah. What's that famous song?

[David Leingang]

Inside Out.

[Dr. Jason Hill]

Inside out, sure.

[David Leingang]

If you go back into the. To the Cats Meow archives, you could find it. But no, I think it would be Garth Brooks seven.

[Ed Gaudet]

No. Nice.

[David Leingang]

Great album. And cool.

[Ed Gaudet]

I appreciate you mentioning the albums, too. Most people don't. That's good.

[David Leingang]

They're slightly older than you might think I am.

[Ed Gaudet]

You're older than me?

[Dr. Jason Hill]

No.

[David Leingang]

Then you think I am.

[Ed Gaudet]

Oh, okay.

[David Leingang]

That.

[Dr. Jason Hill]

No. Was a little emphatic.

[David Leingang]

Sorry.

[Saul]

Yeah.

[Dr. Jason Hill]

Actually, now that you mentioned it, it was.

[Ed Gaudet]

I don't even think I got the words out.

[Dr. Jason Hill]

You had.

[Ed Gaudet]

No.

[David Leingang]

It's a quick one. We'll throw Mighty Ducks, the movie in there since.

[Ed Gaudet]

Okay.

[David Leingang]

She said that was allowed.

[Ed Gaudet]

Yeah.

[David Leingang]

Maybe that E6 album.

[Ed Gaudet]

Yeah. Good.

[David Leingang]

All right. I'm sticking with three.

[Ed Gaudet]

All right. Jason.

[Dr. Jason Hill]

Okay. Red Hot Chili Peppers, Blood Sugar, Sex.

[Ed Gaudet]

I'm on your island. Okay.

[Dr. Jason Hill]

Nice.

[Ed Gaudet]

Yeah, Very nice.

[Dr. Jason Hill]

Love that album. I would say Pearl Jam one because I'm a kid of the 90s. I would also say let's go with the Matrix.

[Ed Gaudet]

Oh, trilogy, Nice.

[Dr. Jason Hill]

Which I'm going to just. Even the third one.

[Ed Gaudet]

Yeah, Yeah.

[David Leingang]

I would do it.

[Dr. Jason Hill]

I would do it.

[Ed Gaudet]

Have you seen the John Wick documentary?

[Dr. Jason Hill]

No. Love me some John Wick, dude.

[Ed Gaudet]

You got to see the documentary.

[Saul]

Classics.

[Ed Gaudet]

Documentaries. Unbelievable.

[Dr. Jason Hill]

Really? Yeah.

[Ed Gaudet]

It's about the making of the movies and everything that went into the whole Wick trilogy. It's out new. It's sort of a plane.

[Dr. Jason Hill]

I don't know.

[Ed Gaudet]

I don't know.

[Dr. Jason Hill]

That's objective.

[David Leingang]

It's a really cool.

[Saul]

Nothing but action.

[Ed Gaudet]

I mean, well, that's. I mean, is he the coolest man alive?

[Dr. Jason Hill]

Truly? Seriously?

[Saul]

Yeah.

[Dr. Jason Hill]

My wife has already fully decided that she's going to try to steal him.

[Ed Gaudet]

He is amazing. He's so cool.

[Dr. Jason Hill]

I was like, she gets one of her. This is A side story, but, like, she has this best friend, and that best friend then decides to pretend that she is Keanu and sends my wife, like, birthday cards and Christmas cards under the guise of Keanu. So I had to sift through her mail and be like, wait, what's going on here, honey? And she's like, that's from Cindy. I don't know. Oh, Cindy.

[Ed Gaudet]

Okay, good, I'm glad You. Is that the name of your wife or.

[Dr. Jason Hill]

No, my wife's name's Elizabeth. But Cindy's the friend that sends her thing.

[Ed Gaudet]

He's under the guise full listener of this program. So. Diano Elizabeth.

[Dr. Jason Hill]

Look at her. Stay away from my wife's friend.

She will leave me for you in a moment. Stay away from her.

[Ed Gaudet]

Jason might go off John Wick on you. Yeah, I know. Oh. Oh, that's good.

[David Leingang]

All right, last question.

[Ed Gaudet]

Riskiest thing you've ever done. David, you seem like a huge risk taker.

[David Leingang]

Skydiving.

[Ed Gaudet]

Oh, yeah.

[Saul]

You did it, huh?

[David Leingang]

Yeah. Three times.

[Ed Gaudet]

Wow.

[David Leingang]

Twice. Not tandem.

[Dr. Jason Hill]

So, you know.

[Ed Gaudet]

Really?

[David Leingang]

I remember specifically in the. You had to take the six hour class before they let you jump by yourself. And they said, these parachutes are really expensive. If the first one is twisted, just cut it, let it go, and pull your backup shoe.

[Ed Gaudet]

I'm like, so you found God, too? Yeah.

[David Leingang]

Like, I'm not sure I want to do this anymore.

[Ed Gaudet]

But that's what they tell you.

[Dr. Jason Hill]

Yeah. Honey, I just learned something about David right now.

[Ed Gaudet]

First of all, what are you cutting it with?

[Dr. Jason Hill]

You have a knife.

[David Leingang]

There's just a clip that.

[Saul]

Yeah.

[Ed Gaudet]

Pull up my. As I'm drop. As I'm dropping to the earth. Let me pull up my switch place.

[David Leingang]

No.

[Saul]

No more skydiving.

[Dr. Jason Hill]

No.

[David Leingang]

Oh, no. All right, now it's been.

[Ed Gaudet]

But you did it. So three times.

[David Leingang]

Pretty bad, man.

[Ed Gaudet]

It's pretty good. That's impressive, Jason.

[Dr. Jason Hill]

Okay, so there's this thing in Vegas. It's called the Exotic Experience. And it's not what it sounds like. It's where you.

[Ed Gaudet]

It's the cars.

[Dr. Jason Hill]

So you drive a Ferrari F150, a Lamborghini, and a Jaguar XJ7.

[Ed Gaudet]

Did you go 200?

[David Leingang]

The straightaway is not long enough for 200.

[Dr. Jason Hill]

Yeah, it's not long enough.

[Ed Gaudet]

How fast did you go?

[Dr. Jason Hill]

I got about 160. Yeah, there's a base car there, and it's a lot of fun. That's probably the riskiest.

[Ed Gaudet]

Did it lift a little?

[Dr. Jason Hill]

Did it feel like it was lifting? It was. So when you're winding through these roads, you're Definitely feeling the side pull as you're trying to go around those corners. So luckily, honestly, of all those cars, the Jag was the best.

[Ed Gaudet]

Really?

[Dr. Jason Hill]

AG was actually better than Lamborghini. And handling the corners.

[Ed Gaudet]

Yeah.

[Dr. Jason Hill]

And the acceleration. Now after that.

[Ed Gaudet]

Did you go fire some weapons?

[Saul]

You didn't.

[Ed Gaudet]

You look, what's your.

[Dr. Jason Hill]

What happens in Vegas stays in Vegas.

[David Leingang]

I think you're gonna need to sponsor a HIMS event to go fire those ruckons.

[Ed Gaudet]

I think that's great.

[Dr. Jason Hill]

That'll be your next podcast, guys.

[Ed Gaudet]

We should do it there.

[Dr. Jason Hill]

It's that.

[Ed Gaudet]

Oh, the range. That'd be fun. All right, hypothetically, what's the funnest weapon you've ever fired? Assuming you ever did, by the way.

[Saul]

You guys know that Ed was a marksman, right?

[Ed Gaudet]

No. Yeah, marksman in the military. Army guy.

[David Leingang]

I've got about 10 total sessions with a Glock under my belt and that's it.

[Dr. Jason Hill]

Oh, okay.

[Saul]

Glock, that's fun.

[Ed Gaudet]

Impressive.

[Dr. Jason Hill]

My dad is a navy guy and has an unbelievable amount of guns, so I fired a lot of weapons. That's awesome. And my favorite weapon that I fired actually telling the story about this was my friend of mine. Doug had bought this weapon off of a second hand. Sorry. That was a Japanese anti aircraft rifle. And it fired a very special piece of ammunition that we had to make bullets ourselves. And it has like one of those little anti aircraft adjustable sights to up or down on. And so it has a two point sight. You can go up or down depending on this. And it has actually in Japanese, the speed of the plane. So you can then figure out like if you fired the bullet, like if it would actually hit the plane. Wow. So we built about 50 bullets, got a. A couple of adult beverages under our belts and friends. A plane. Did you have a plane? No, unfortunately not. Stationary targets. We were in our mid-20s and David's like, Doug's got a farm, so there's no one around. Okay, so a gun. Size of home, kids, St. Aaron, gun safety, things we're adhered to. And I remember firing this weapon and it almost blew my shoulder off. It was this giant, giant package on the back of it. And it's a straight wooden butt on the back of the gun. And so that force just completely transferred back into my shoulder. And I remember falling backwards after firing it. And literally I looked at my friend Jesse who was right after me, and I was like, all right, your turn.

[Saul]

I'm done with that one.

[Ed Gaudet]

50 cals are fun to 50.

[David Leingang]

Cows are fun.

[Ed Gaudet]

Yeah. And the law, if you get like a, hey, just don't stand behind the law.

[Dr. Jason Hill]

And I've never fired a law. That would be really fun. 50 Cal is fun in a very controlled environment on a point mounted.50 cal. Yeah.

[Ed Gaudet]

Well, you didn't carry one.

[Saul]

Yeah.

[Ed Gaudet]

Nothing. Nothing like carrying a bear.

[Dr. Jason Hill]

Arnold Schwarzenegger. Right?

[Ed Gaudet]

All right, I think we're ready to wrap.

[Saul]

Yeah. That was fantastic.

[Ed Gaudet]

Yeah. Thanks for joining us. Yeah.

[Dr. Jason Hill]

Thank you guys for having us.

[Saul]

Appreciate it. And by the way, if anybody wants to get in touch or learn more about the work that you guys do, where can they do that?

[Dr. Jason Hill]

They can email me or David Jahiller.org.

[David Leingang]

And I'm David.Liongang L E I N G A N [e-mail] Awesome.

[Ed Gaudet]

You might make that easier. Well, yeah, we'll leave that in the show notes. Guys, he's been there a while.

[Saul]

You guys don't have to figure out how to spell it. It's in the show notes. Check those out for the show for the ways to get in touch. This has been a lot of fun. Yeah, they appreciate it.

[Dr. Jason Hill]

Thanks, all.

[Ed Gaudet]

I appreciate you.

[Saul]

Foreign.

[Ed Gaudet]

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 [e-mail] that's C-E N S I N E T dot com. I'm your host, Ed Gaudet. And until next time, stay vigilant because Risk never Sleeps.