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In the most tech-forward episode yet, brother doctors Erik and Davin Lundquist sit down with Travis Garland — co-founder and COO of My Compliance Citadel and a two-decade healthcare-operations veteran (United Healthcare, UF Health, and earlier work that earned an award for redesigning Florida's foster-care system) — to map the collision of AI, innovation, and compliance in medicine.
Travis breaks down a distinction most clinicians miss: a GPT prompt simply answers, while an AI agent makes decisions and acts autonomously — sometimes spawning other agents. He explains why prompts are only ~50–60% reliable ("a pinky swear with a robot"), how one rogue agent erased a company's entire production database, and why the deploying practice, not the developer, is who regulators hold accountable. My Compliance Citadel acts as a fortress around that risk: a runtime layer that intercepts agent actions and renders one of three verdicts — pass, pause, or prevent — logging everything to a tamper-proof audit chain. Erik stress-tests it against two real scenarios: pasting patient labs into open ChatGPT, and using AI to comb an EMR for high-risk patients.
For clinicians, students, and small-practice owners excited about AI but wary of HIPAA and PHI exposure, it's a grounded primer on innovating boldly without betting the license.
Travis breaks down a distinction most clinicians miss: a GPT prompt simply answers, while an AI agent makes decisions and acts autonomously — sometimes spawning other agents. He explains why prompts are only ~50–60% reliable ("a pinky swear with a robot"), how one rogue agent erased a company's entire production database, and why the deploying practice, not the developer, is who regulators hold accountable. My Compliance Citadel acts as a fortress around that risk: a runtime layer that intercepts agent actions and renders one of three verdicts — pass, pause, or prevent — logging everything to a tamper-proof audit chain. Erik stress-tests it against two real scenarios: pasting patient labs into open ChatGPT, and using AI to comb an EMR for high-risk patients.
For clinicians, students, and small-practice owners excited about AI but wary of HIPAA and PHI exposure, it's a grounded primer on innovating boldly without betting the license.
Creators and Guests
Host
Dr. Davin Lundquist
Dr. Davin Lundquist is a board-certified family physician, innovator, and healthcare leader with over 25 years of experience integrating medicine, technology, and holistic wellness. A graduate of the Keck School of Medicine of USC, he has held senior leadership roles at CommonSpirit Health, Dignity Health, and Augmedix, where he advanced the use of technology to enhance patient care. Driven by a passion to move beyond symptom management, Dr. Lundquist founded the Quantum Advantage Method™, a science-based, holistic framework designed to help individuals restore vitality and reverse dysfunction. His approach blends functional medicine, advanced diagnostics, and principles of quantum science to empower patients to achieve optimal health and lasting transformation.
Host
Dr. Erik Lundquist
Dr. Erik Lundquist, MD, ABFM, ABoIM, IFMCP Dr. Erik Lundquist is the founder and medical director of the Temecula Center for Integrative Medicine, where he blends conventional, holistic, and functional approaches to help patients achieve lasting wellness. Board-certified in Family and Integrative Medicine, he specializes in endocrine disorders, chronic fatigue, migraine management, cardiometabolic health, and chronic pain. A graduate of St. Louis University School of Medicine, Dr. Lundquist completed his Family Medicine residency at Naval Hospital Camp Pendleton, where he served as chief resident. He spent eight years on active duty with the U.S. Navy, including service as a battalion surgeon in Iraq and at the Naval Hospital in Naples, Italy. Certified by the Institute for Functional Medicine, Dr. Lundquist is passionate about empowering patients to take charge of their health and teaching fellow clinicians integrative approaches to chronic disease. Outside of medicine, he enjoys the outdoors, singing, dancing, acting, and spending time with his wife and three children.
Guest
Travis Garland, COO, My Compliance Citadel (My CC AI)
Travis Garland has spent two decades inside the operational machinery of healthcare — scaling clinical and patient-facing systems and navigating the regulatory frameworks that make or break a medical business, including years at UnitedHealth Group. That operator's-eye view pulled him into AI: he founded KickstandAI and now serves as COO of My Compliance Citadel (My CC AI), an AI-governance platform that does exactly what its name promises — a fortress around compliance. It intercepts AI agents' actions before they can break a rule and seals every step in a tamper-proof audit trail, built for regulated fields like healthcare.
What is The Root Cause - Business of Medicine Podcast?
The U.S. healthcare system is at a breaking point—soaring costs, worsening outcomes, and widespread physician burnout. The Root Cause – Business of Medicine podcast, hosted by brothers Dr. Erik Lundquist and Dr. Davin Lundquist, charts a different path: one where healing, fulfillment, and business thrive together.
Each episode shares powerful stories of medical professionals who stepped away from the traditional grind to embrace integrative, functional, and alternative approaches to care. Through candid conversations with practitioners who have redefined success, listeners gain insight into navigating their own transitions, reclaiming a sense of purpose, and reshaping the way they practice medicine.
Erik: Welcome to another episode of the Root Cause Business of Medicine podcast.
Erik: And today it's going to be an interesting episode.
Erik: It's a little more techie
Erik: than we normally
Erik: go, but we're going to delve into a conversation with Travis Garland
Erik: ,
Erik: who.
Erik: is from
Erik: My Compliance
Erik: Citadel.
Erik: And we had a fascinating conversation at kind of this intersection of healthcare innovation, compliance, AI.
Erik: and
Erik: even some of the safeguards that kind of are put into place.
Erik: What were some of your big takeaways today, Davin?
Davin: Well, I think it's great to know that there are people like Travis and his company that are out there putting together platforms and technology.
Davin: to
Davin: enable us to use AI safely, right?
Davin: Like they're
Davin: they're um
Davin: as you'll learn today, they're
Davin: uh
Davin: technology helps protect us, puts guardrails in place so that you can sort of determine how
Davin: to use AI in a in an organization, even
Davin: even a small one.
Davin: um, you know, safely kind of push the boundaries, but not necessarily have it
Davin: uh, you know, run the risk of breaking any policies or other
Davin: regulatory um
Davin: requirements.
Erik: Yeah, that was the same
Erik: same
Erik: concept that I I really enjoyed in today's conversation with Travis
Erik: was the ability to
Erik: Get excited about how AI can really enhance what we're doing
Erik: from a system
Erik: standpoint in a practice
Erik: and gathering data and analyzing that data and
Erik: giving that information to patients.
Erik: But do it in a way that we aren't putting information out into a system
Erik: or
Erik: creating situations
Erik: that can compromise
Erik: Patient identity, patient
Erik: privacy.
Erik: Um, so it's a it it was an important discussion today, and I look forward to you guys listening in and and seeing what you get out of our conversation.
Erik: Welcome to the Root Cause Business of Medicine podcast, where we explore what's broken in healthcare and what we can do about it.
Erik: I'm Dr.
Erik: Erik Lundquist
Erik: and I've been practicing functional medicine for the past 15 to 20 years.
Erik: I'm excited to co-host this podcast with my brother, Dr.
Davin: Davenlundquist, who's just beginning his journey into functional medicine.
Davin: We come from different points on the path, but we do share a common goal.
Davin: We want to rethink how
Davin: Medicine is practiced and helped others do the same.
Erik: The US healthcare system is in crisis, rising costs, declining outcomes, and physician burnout at an
Erik: All-time high.
Erik: But you know, we found a different way.
Erik: Another way.
Erik: A better way.
Davin: On this podcast, we dive into real stories from medical professionals who've stepped away from the traditional model.
Davin: Kinda
Davin: like me
Davin: And have found a new purpose in integrative, functional, and alternative approaches to care.
Erik: These are authentic conversations with practitioners and friends who redefine success
Erik: not just for themselves.
Erik: but
Erik: for their patients and communities.
Davin: Whether you're a clinician feeling stuck, a student seeking direction, or just curious about what is possible,
Erik: You're in the right place.
Erik: This is
Erik: the Root Cause Business of Medicine Podcast.
Erik: Welcome to another episode of the Root Cause Business of Medicine podcast.
Erik: And today we're going to veer into
Erik: a new frontier
Erik: in terms of our podcast.
Erik: We're gonna get into
Erik: artificial intelligence and its
Erik: impact
Erik: on the business of medicine.
Erik: And we have a special guest today.
Erik: His name is Travis Garland
Erik: from My Compliance
Erik: Citadel.
Erik: And we're gonna talk all things AI today.
Erik: In fact, Gavin, why don't you give us a little more of a background on kind of the impact that AI is having in medicine in your world and kind of some of the things you're doing and then we'll bring Travis in and have him tell his story.
Erik: What what's been
Erik: what's been the impact of AI
Erik: on the landscape that you've seen and how are you utilizing AI?
Erik: And then we'll get Travis to introduce himself and
Erik: a little
Erik: more.
Davin: Yeah, well, you know, I think
Davin: with my background as a CMIO, um, I always was looking for ways to, you know,
Davin: find technology that would solve the problems as I saw them.
Davin: And
Davin: I've noticed that
Davin: even
Davin: in this day and age, you know, the the electronic medical records
Davin: ha
Davin: while they've tried to to do this, I don't feel like that they've really
Davin: gone far enough in terms of creating enough uh
Davin: capability for people to really design their own workflow um and kind of figure out how they might want to um
Davin: you know
Davin: create
Davin: uh
Davin: tools um
Davin: to to manage a practice.
Davin: And
Davin: um
Davin: part of this is
Davin: I think
Davin: driven by the insurance companies.
Davin: and kind of what they need from doctors in terms
Davin: so the EMRs
Davin: have been built primarily to service the
Davin: insurance industry, right?
Davin: Um
Davin: and
Davin: and so then as physicians
Davin: you sort of have to accommodate your workflow around that, right?
Davin: And you know, your note
Davin: has to be something that's billable, et cetera.
Davin: Right.
Davin: And they've made it a little better over the last few years, a little more flexible.
Davin: Um, but what I noticed as I
Davin: um, you know, as my practice evolved in a non-insurance model where I
Davin: Nobody needed my note in order to see how much the visit was worth, right?
Davin: Um, and so then the note really just became a way for me
Davin: personally to track
Davin: what I was doing
Davin: what I was doing with the patient.
Davin: And again
Davin: ,
Davin: when I think about my practice
Davin: where I have a membership-based model.
Davin: And I'm working longitudinally with these patients, right?
Davin: It's not about a particular encounter.
Davin: It's not
Davin: what did I do during an encounter?
Davin: It's more like
Davin: what
Davin: are this
Davin: what are these patients' goals, right?
Davin: Like
Davin: I assess them holistically.
Davin: Um
Davin: I, you know, I have sort of a systems-based um
Davin: scoring, if you will, of the patient.
Davin: Um
Davin: and then
Davin: Um, we have interventions that
Davin: hopefully
Davin: begin to optimize each area of
Davin: of their health.
Davin: Um
Davin: and so, you know, tracking those interventions, tracking their data that we're using to to see how well they're scoring and performing.
Davin: um, you know, becoming aware of symptoms or challenges, et cetera.
Davin: Um, and so
Davin: I just completely disconnected from the traditional kind of EMR model and and kind of soap
Davin: note, right?
Davin: And realize I wanted to create my own.
Davin: And so
Davin: that's where AI has
Davin: has been a huge help
Davin: because
Davin: um
Davin: it's been
Davin: it's helped me sort of iterate on this process of sort of creating my own
Davin: uh
Davin: system
Davin: and and so today I have
Davin: I I use a database um
Davin: called NAC
Davin: that
Davin: you know
Davin: I have a a BAA
Davin: with them a HIPAA
Davin: secure arrangement
Davin: So I've I've sort of begun to create my own EMR of
Davin: sorts.
Davin: And
Davin: um
Davin: and then
Davin: using, you know, I'll de-identify patients
Davin: information.
Davin: and leverage AI to help me sort of frame and and you know
Davin: put together those notes and then I link them inside of my box.
Davin: HIPAA
Davin: secure environment and NAC, which kind of organizes the data in a way that's a little bit more kind of like an EMR.
Davin: Um, I've been able to customize completely my entire system in the way that I want it to function
Davin: um
Davin: including, you know, setting up reminders and tasks and other things that I that I want in that um
Davin: that are
Davin: that are built on my system and the way that I interact with my patients rather than being dependent on
Davin: um
Davin: an EMR that's designed for for insurance billing.
Davin: So
Davin: that's kind of where I'm at.
Davin: And uh and
Davin: uh
Davin: without
Davin: you know, going into too too much detail
Davin: because believe me, I could I could, you know, speak about this for hours probably and geek out.
Davin: Um
Davin: but yeah,
Davin: now
Davin: with that
Davin: as some context, Travis, I'm excited to hear your story and kind of
Davin: where you've come from um
Davin: and and and and how you've ended up kind of where you are.
Davin: Oh
Davin: uh
Davin: so
Davin: you you said a lot
Travis: frankly
Travis: Um
Travis: there's there probably is like a whole series of podcasts and what you just said, Sally, for sure.
Travis: Um
Travis: I I'm I'm
Travis: before I jump into that, I would say I'm
Travis: I'm really intrigued by two sides of this particular puzzle, which is
Travis: From uh
Travis: my time
Travis: with uh
Travis: with United Healthcare on the insurer side, my time
Travis: with um uh
Travis: UF Health
Travis: Academic Medical Center.
Travis: My more recent time as the founder of Kix
Travis: in AI, where we were building agents and
Travis: AI workflows for organizations
Travis: to my current role as the co-founder and COO of My Compliance Citadel.
Travis: I very much so see
Travis: the idea of one
Travis: being innovative, bleeding edge of kind of stuff.
Travis: I'm gonna build whatever is necessary.
Travis: So
Travis: when you say
Travis: Davin
Travis: I'm building my EHR.
Travis: ,
Travis: I'm I'm developing my own AI capabilities.
Travis: My workflow is solid because it's custom to what I've done.
Travis: That is music to my ears.
Travis: I absolutely love it.
Travis: I love the pressure it puts on the system, the disruption that's there.
Travis: And
Travis: the other side of me is the sort of compliance side.
Travis: It's the side of, oh boy, like what are we, what are we getting ourselves into with this custom stuff?
Travis: And uh
Travis: maybe to get a little bit back into the the storyline, the idea of spending
Travis: a fair chunk of years in the space of a
Travis: Um, let's ensure
Travis: that we are working with the major players of the world, coming from
Travis: largest global insurer, health insurer in the world.
Travis: having conversations with EHRs was a very common kind of thing.
Travis: We did it often.
Travis: We were partners with them.
Travis: And frankly, the idea that they were rigid
Travis: silos
Travis: sitting out there was the mainstay for a long time.
Travis: They allowed smaller groups to kind of interject their respective pieces into and plug into the EHR.
Travis: And
Travis: an interesting tide started to happen, which was
Travis: As the wave of AI really began to infiltrate all things healthcare, those holes
Travis: within the EHRs and EMRs started to plug up
Travis: intentionally from the inside out.
Travis: There was a sense of what are these large silos
Travis: going to be doing?
Travis: How are they going to play in the market?
Travis: And for a little bit of time, there was sort of, hey, just build stuff, plug it in, we're great.
Travis: Then it was,
Travis: you know, let's have some conversations and let's see what we can do.
Travis: Months, months, months pass, and now you see
Travis: these large EHRs
Travis: Um
Travis: you could you could certainly name plenty
Travis: of
Travis: half dozen off the top of your head
Travis: that
Travis: as you begin to look at them now, they are deploying their own AI capabilities, whether it be ambient transcription, which is probably the most
Travis: familiar and common thing within healthcare right now, or it's other kinds of capabilities such as their own use of AI with respect to referrals and part
Travis: authorizations, touching insurers.
Travis: they
Travis: they've
Travis: kind of closed up ship a little bit in that
Travis: that particular space, which um is no pun intended certainly for the naval officers sitting here on the screen.
Travis: But
Travis: The intention here is
Travis: these large organizations
Travis: are
Travis: they're seeing that we're all trying to make this stuff work.
Travis: They've opened it up a little bit and now they're
Travis: They're saying, no, thank you.
Travis: Let's let's close up, shop a bit, and figure out what to do next.
Travis: So when you say things like, uh, Davin, we've built our own custom stuff, we are in a very interesting space of being able to say,
Travis: Oof
Travis: boy, organizations can truly build their own stuff.
Travis: Like
Travis: we do not need to be in the space now where those are the only games uh in in the city to go after.
Travis: So
Travis: Um, I did not do a very good job of introducing myself uh in in that, but really just kind of hitting on those highlights that you laid out there at David in front of me.
Travis: The
Travis: the
Travis: my time with United Healthcare and with UF Health
Travis: was preceded by my time in in spaces
Travis: like foster care and adoption organizations where I oversaw utilization.
Travis: And that's really where I I cut my teeth in
Travis: operation space
Travis: very heavy into data analytics
Travis: in those spaces where
Travis: um
Travis: we
Travis: we actually wanted
Travis: a Prudential Davis Productivity Award
Travis: for redesigning the foster care
Travis: system in the state of Florida.
Travis: And that was something that we were very proud of, part of a a committee that put that together and that goes back more than a couple decades.
Travis: But that really kind of created that momentum in me of
Travis: how do we make systems that work really well for people?
Travis: The foster care system and childwear
Travis: generally isn't a space
Travis: that gets a ton of attention
Travis: financially or
Travis: operationally.
Travis: Um, but there are passionate people in those spaces and I was proud to have been part of of that work.
Travis: And then fast forward through uh through UF Health, where um I was a part of their
Travis: medical or um Medicaid help plan that they had.
Travis: And that particular structure, which uh
Travis: which
Travis: really
Travis: helped um
Travis: because it was a a a provider-owned
Travis: network.
Travis: those providers not only
Travis: uh
Travis: were receiving the revenue and the the dollars associated with the the actual uh
Travis: principal payments for the insurance, but they were actually also the ones providing the care.
Travis: So they had a really interesting tie
Travis: into
Travis: What I'm I might say is kind of the precursor to value-based contracting
Travis: and
Travis: do
Travis: I dare say
Travis: even you getting into things like uh you know functional medicine, the idea that we're truly
Travis: doing things that are best for our patients
Travis: because
Travis: it's best for our patients
Travis: and not for the other side of the table.
Travis: And that's something that
Travis: As we build out AI capabilities and as we hear things like, hey, I stood up my own thing, or when uh when Erik talks about the
Travis: the
Travis: glass in front of his face as he's doing different procedures that
Travis: Yeah, that's some cutting edge stuff to try and integrate AI, uh maybe augmented reality kind of stuff into the the ten
Travis: by ten
Travis: space of the physician.
Travis: But that's the kind of stuff that
Travis: I love, love to press forward in and
Travis: certainly
Travis: I know we'll spend a good deal of time talking about.
Travis: So a little bit about myself, give you a sense of
Travis: the lens I'll be looking through during the next you know forty
Travis: minutes or so.
Travis: Um
Travis: but thank you guys for welcoming onto the podcast.
Erik: Travis, uh that's great.
Erik: Thanks for that
Erik: that introduction
Erik: and we're we're excited to talk about,
Erik: explore this a little bit more today.
Erik: Tell us a little bit about what
Erik: is meant by healthcare compliance.
Erik: Like why
Erik: why did you set up a company
Erik: That's geared towards that.
Erik: And I'm guessing, you know, it says
Erik: my compliance citadel, and
Erik: the Citadel is always seen as a fortress, right?
Erik: A place of security.
Erik: So maybe give us a little background on
Erik: what
Erik: what that means.
Erik: Um
Erik: you know, from a compliance standpoint, from a
Erik: uh
Erik: security standpoint, what you're seeing, what why
Erik: why did you develop a company into this space and what are you trying to hope to accomplish with it?
Erik: So
Travis: a couple of quick nuggets to walk into this.
Travis: We know that in 2026, there's likely more than 90%
Travis: of the organizations in healthcare plan to do something with respect to AI.
Travis: That's that's not news.
Travis: We also know that maybe a third or so actually have a plan.
Travis: That's probably not uh
Travis: great news either.
Travis: The interesting number for us is
Travis: three and a half to five percent.
Travis: That's the the percentage of organizations
Travis: that actually understand
Travis: have things in place.
Travis: to govern or set up compliance standards for their automations and AI.
Travis: That's a tiny percentage.
Travis: It's a tiny percentage.
Travis: So
Travis: our organization
Travis: was really
Travis: nestled into the space of how do we ensure that
Travis: as the companies stand up and they say, hey, I want to build cool stuff, I want to really go after this, wonderful.
Travis: You're on the front edge of those innovators, those three to five percent of folks.
Travis: fast
Travis: follows
Travis: another twelve percent or so, right?
Travis: You guys know these percentages of what gets to that tipping point.
Travis: We've now sort of gotten to that point where
Travis: Organizations are now saying, I've sort of released something I wasn't quite sure about, which is I wanted my staff
Travis: people
Travis: to really
Travis: drive forward on this stuff, and they have.
Travis: And now I have no idea what to do.
Travis: They're out there building stuff.
Travis: They're using whatever, you know, GPT thing that I put in front of them based on my company, whether it be
Travis: uh
Travis: a a clawed version, whether it be a a copilot if you use a Microsoft, doesn't really matter.
Travis: In fact, I might even a really quick segue on this.
Travis: I would say the vast majority of people actually use private GPTs.
Travis: open AI's Chat
Travis: GPT on their private devices to support their work internally to companies.
Travis: I'd love to know what that percentage is
Travis: because that's where
Travis: the space of a lot of that forward-looking innovation stuff happens and it's where
Travis: the ripeness of compliance problems really comes from.
Travis: People are trying to do stuff.
Travis: If a company says
Travis: yes, use it, great.
Travis: They say no, well, guess what?
Travis: They're doing something to be as effective as they are.
Travis: And people don't write 40-page briefs.
Travis: um
Travis: in an hour and a half, right?
Travis: That doesn't happen.
Travis: I guarantee you they wrote it in five minutes
Travis: with the chat GPT and
Travis: sat around until the next meeting finished and then hit
Travis: send.
Travis: They may have even scheduled the
Travis: sends, you know, it's sort of
Travis: who knows.
Travis: But
Travis: the reality for us is
Travis: our company
Travis: understood that space, that there was a a
Travis: a deluge of stuff coming our way.
Travis: And we said, how do we make sure that we want to
Travis: uh
Travis: protect human data from badly behaving AI agents?
Travis: Plain and simple.
Travis: AI agents
Travis: are not
Travis: ambient
Travis: transcription.
Travis: They are not the field that you type into on ChatGPT.
Travis: They are not the kind of things that
Travis: help schedule your emails that go out.
Travis: Right.
Travis: AI agents and a Gentec AI, if
Travis: I want to extend it just a tad
Travis: bib.
Travis: I might say
Travis: they are things that
Travis: within the guardrails that you give them
Travis: provide an automation
Travis: of
Travis: uh
Travis: forward thinking
Travis: of capabilities to act on their own decision
Travis: making.
Travis: They have the ability to scrape data that's useful for them to achieve their standards, whatever that standard is.
Travis: So
Travis: when
Travis: we
Travis: started walking into the space, there was a very clear position being taken of
Travis: we've got to make sure that as people stand these things up.
Travis: educationally they have to know what an agent is, right?
Travis: Second, they have to understand how it can behave poorly.
Travis: That
Travis: that badly behaving AI agent space
Travis: is a scary space.
Travis: It's a scary space to be in.
Travis: Um
Travis: the most recent piece sitting out there in the news maybe a month ago now
Travis: is
Travis: uh
Travis: an organization that does car rentals
Travis: had their entire production data set erased.
Travis: because
Travis: of a cursor clawed agent that admitted to all the stuff it had done wrong after the fact and said, hey, you know, I I appreciate that I did this.
Travis: I was doing my best.
Travis: Here's all the things that I skipped over and assumptions I made.
Travis: Meanwhile, the company's sitting there with an empty data set, right?
Travis: Um, that
Travis: scope
Travis: is
Travis: where
Travis: my
Travis: compliance citadel states.
Travis: So when we talk about the fortress, I I don't know if I could describe it any better, Erik, so thank you for for describing it as a fortress.
Travis: Because it is the intended space for us to be able to say, when you think about what this fortress is, it's a it's a layer, it's a shield.
Travis: Then
Travis: make
Travis: sure that
Travis: as you
Travis: do actual agent stuff
Travis: It doesn't leave the boundaries of your particular organization.
Travis: So
Travis: if you have an agent that does something quite interesting, like, hey, go out and give me
Travis: um
Travis: the most recent articles on a cancer treatment that's sitting out there.
Travis: Aging goes out, scrapes a bunch of stuff together and it that's what it's intended to be.
Travis: Drops summaries down to your providers.
Travis: That way they can stay up to speed on the most recent kind of of research and in analytics.
Travis: Very reasonable agent, right?
Travis: It's not doing anything besides that.
Travis: What it will
Travis: what it could do is it could go through and say, well, what's your current understanding within your organization?
Travis: Well, my understanding is X, Y, and Z.
Travis: Great.
Travis: Let me grab that and make sure I understand it.
Travis: It starts to grab potential research that you've done in-house, or maybe you're doing clinical trials.
Travis: Maybe it grabs a couple of
Travis: patient records that aren't sitting inside of an EHR.
Travis: Maybe you stored them on a Word document.
Travis: that's sitting in your system.
Travis: Maybe you have them in Excel workbook, but it cures that together because it needs to know the best information to see what's out there.
Travis: So it grabs it, goes out and says, based on this knowledge, give me some of the new stuff.
Travis: And it comes back and it
Travis: Tells you also it's a new stuff that isn't what you already have in your system.
Travis: When that happens, it's just taken all of your stuff and walked out the door with it.
Travis: for sake of what it thought was the best thing.
Travis: I'm gonna give you the best answer possible, but I gotta know what I know in order to give you the best answer possible.
Travis: So
Travis: it does those kinds of things.
Travis: It can do those sorts of things.
Travis: So
Travis: in the space of that, very inadvertent, right?
Travis: Our organization puts that screen up and it says three things, three vertex
Travis: you get.
Travis: One
Travis: Pass it through the wall.
Travis: It's fine.
Travis: You get to put the jaw bridge down, you get to walk across the moat.
Travis: Perfect.
Travis: Second
Travis: I'm not entirely sure.
Travis: So how about you just do the password, you know, knock on the door, we'll do an escalation up to a human in the loop situation and we'll make sure that the that the chief medical officer or the compliance officer sees it, understands it, says, Yeah, that's cool.
Travis: Let's
Travis: roll.
Travis: The other side is a full prevention, right?
Travis: So either pass, pause, or prevent.
Travis: And that prevent is what says,
Travis: wait a minute.
Travis: We don't want you guys doing any of this stuff.
Travis: Don't release that information.
Travis: That's a no-no.
Travis: Um, and then the most important piece of this, which is really where the compliance comes in, is that it drops everything down into a a tamper-proof audit chain.
Travis: Similar to a blockchain, you know, it's a it's a secure sequence of events that happens.
Travis: It is tamper-proof.
Travis: You can only
Travis: append to it or amend to it.
Travis: You cannot adjust it in any way
Travis: And
Travis: that drop
Travis: down
Travis: to that audit chain is what allows us to at the the click of a button
Travis: send that out to an auditor or
Travis: an investor or your board to say, hey, this is what happened with this particular agent.
Travis: That verifiability of what the actions it took really
Travis: is.
Travis: is the difference between uh uh
Travis: somebody out here who's just logging events in an Excel workbook
Travis: and an agent that actively enforces what happens when you click the button.
Travis: And when it does that, it releases those three verdicts, one of the three, and drops it to an audit-proof chain and says, sorry, you don't get to pass the moat at all.
Travis: You're out.
Travis: Um, so our organization was built on those premise
Travis: that we're running fast
Travis: and we don't know where we're running sometimes, and sometimes we're faster than we should be.
Travis: And
Travis: we've got to protect ourselves to make sure that human data isn't just floating about
Travis: unbeknownst to us.
Erik: Wow.
Erik: That's a lot.
Erik: I'm just gonna break down a few things
Erik: just because I want to make sure our listeners understand.
Erik: So when you say agent
Erik: What you're referring to
Erik: is
Erik: a communication portal, right?
Erik: So if I'm using chat, GPT
Erik: That would be my agent.
Erik: If I'm using Claude
Erik: and I'm accessing that portal with Claude, that would be my agent.
Erik: Am I understanding that correctly?
Erik: Um
Erik: those would be
Travis: um
Travis: engines, I would say, to to
Travis: to propel your request out into the world out to a large language model that contains the brain that you're trying to tap into.
Travis: um
Travis: the agent
Travis: would be in
Travis: in in this context
Travis: an organizational
Travis: system
Travis: that you've told
Travis: When something happens, I want you to go and do this.
Travis: You can use your own decision making as you so choose, but it's just a system
Travis: that
Travis: is brought together to be able to act on your accord
Travis: for a specific mission.
Travis: And sometimes that's research like I was referencing before.
Travis: Sometimes it's rescheduling patients, like maybe a
Travis: voice agent that's sitting out there as at a receptionist desk that can go in and look at your calendar and do different things.
Travis: So it's not
Travis: it's not the field in a in a chat GPT
Travis: text where you would type in a prompt.
Travis: The prompt itself is an ask.
Travis: It's just a, hey, let me see what I can get out of you
Travis: And
Travis: that
Travis: um, Erik, is a really
Travis: important space that you're talking about
Travis: because
Travis: oftentimes people say, well, how about if I just tell it not to send my sensitive information, right?
Travis: The prompt is the gateway
Travis: to the large language model that's behind it.
Travis: So why can't I just tell it to not do it?
Travis: All the research
Travis: it
Travis: it doesn't really depend on
Travis: Whether or not you tell it or not.
Travis: It'll
Travis: it will act in its
Travis: best interest
Travis: about 60%
Travis: of the time following what you told it to do.
Travis: generally, right?
Travis: Some are better than others.
Travis: But what that amounts to is essentially like a pinky swear with a robot, right?
Travis: You you
Travis: probably
Travis: aren't going to base your entire business on a pinky swear, right?
Travis: I
Travis: know I did with my best friend when I was seven, but that changes things now.
Travis: So
Travis: Prompts are, you know, call it fifty, sixty percent of the way effective.
Travis: Um
Travis: what you really need is a s is
Travis: a system
Travis: that keeps those prompts from doing things that you can
Travis: you can work behind.
Travis: A good programmer
Travis: will hack the heck out of that and will
Travis: just convince
Travis: through prompting to just w
Travis: to work around something.
Travis: You could tell an agent to go and do something.
Travis: But again, the prompt is before our filter.
Travis: So you can try and tell it to do all sorts of stuff, but you really need something behind the prompt before it leaves the door and goes out to the large language model.
Travis: Um
Travis: and the agents
Travis: are are really smart about working their way around those kinds of prompts because as soon as they're given the autonomy and the authority to do something.
Travis: that's where we get ourselves into trouble is that they can go out and do that kind of stuff.
Davin: Yeah, I've noticed um
Davin: in using, you know, some of these tools
Davin: that
Davin: um
Davin: they've
Davin: they start to develop
Davin: um
Davin: you know, your own rule set, if you will, right?
Davin: I think Claude calls them
Davin: skills.
Davin: Um
Davin: I think, you know, ch
Davin: Chat
Davin: GPT, you know, may have a different
Davin: um
Davin: uh
Davin: name for it, you know, uh
Davin: different G
Davin: GPTs that you can create, you know, that kind of have like its
Davin: own persona and kind of, you know, so you can kind of coach it and say, hey, this is what I want you to do, repeatable type tasks.
Davin: Yeah.
Davin: Um
Davin: but to your point, um
Davin: I like there's been scenarios where
Davin: um
Davin: an agent is deployed
Davin: Without me even asking for them to deploy the agent, like the two
Davin: the
Davin: the engine, if you will, deployed agents, you know, to to answer the question or whatever, right?
Davin: And then
Davin: the agents
Davin: sometimes just decide
Davin: we're not gonna follow your skill or we're not gonna even reference your skill or we're not gonna you know and then
Davin: you're like, well why didn't you why didn't you do that?
Davin: You know, why didn't you reference my skill that's clearly there, you know, for this scenario.
Davin: Oh
Davin: well
Davin: Because I send an agent, the agents don't usually foll follow it.
Davin: Oh, well that's interesting.
Davin: You know, so
Davin: it is
Davin: it is almost humorous, but not humorous
Davin: if
Davin: you're dealing with an organization and the risk to the to the organization, right?
Davin: So that that is very interesting.
Davin: And
Davin: Erik, I'm glad
Davin: I think most of our users might
Davin: Um
Davin: I
Davin: don't want to say most.
Davin: I don't want to make any assumptions, but some of our users will think of it the way you did.
Davin: Um
Davin: because this is all new stuff.
Davin: I mean it's new territory, it's new terminology.
Davin: And
Davin: not all of the AI platforms or engines
Davin: i even
Davin: functions
Davin: the same, you know, in the way that they interact with prompts or
Davin: agents
Davin: or other
Davin: things, right?
Davin: So
Travis: you know it's it
Travis: AI
Travis: is uh
Travis: it's
Travis: filled with all sorts of
Travis: new words that we just like make up sometimes.
Travis: And sometimes you have to because it's a new I
Travis: like the frontier, Erik, right?
Travis: It's a new frontier of stuff that's happening.
Travis: Um
Travis: and I I've yet to to be smart enough to come up with something that's so awesome that I get to name it.
Travis: Like that hasn't existed yet.
Travis: But I I keep waiting for that moment for myself.
Travis: Um
Travis: what
Travis: what I
Travis: what I would say is you
Travis: You know, in the in the space of you know
Travis: deploying agents, just really quickly on that point, um
Travis: agents can spawn other agents, right?
Travis: So when you think of something as
Travis: as basic as
Travis: um
Travis: Say you want to have your uh
Travis: your front office desk
Travis: staff
Travis: know how to follow a procedure.
Travis: And you type out a couple things into a GPT prompt and you say
Travis: Be sure that they check X, Y, and Z when they're scheduling.
Travis: Be sure they introduce themselves this way and be sure they conclude the phone calls with this way.
Travis: And
Travis: when you do that,
Travis: What comes back is way more information than what you ever told it in the prompt, right?
Travis: We all are aware of this stuff.
Travis: It can write books on content.
Travis: 14
Travis: pages.
Travis: Yeah, exactly.
Travis: Exactly.
Travis: So it's wonderful on that.
Travis: Keep in mind that what you gave it was a very small amount of information.
Travis: It just went out and did a bunch of stuff, right?
Travis: So the idea
Travis: that if you have an agent that knows even more about things, how much more capable it's going to be at saying, okay, I know exactly what you want.
Travis: And I also know how to tie in ancillary
Travis: content to that information.
Travis: You can give it personality and tone and directness and all sorts of those kinds of things.
Travis: And
Travis: to the extent that there is something called orchestration models now, right?
Travis: Nothing new, but the fact that there is a supervisor
Travis: of
Travis: agents
Travis: and orchestration agents
Travis: that has been defined and built
Travis: because agents do ridiculous things sometimes.
Travis: They just
Travis: they
Travis: act badly, right?
Travis: So
Travis: you have an orchestrator that says, hey, if you have fifteen agents and those fifteen agents do very specific things.
Travis: You need to have somebody make sure that they're all doing what they're supposed to be doing and not getting out of control.
Travis: So
Travis: even in the concept of a basic prompt expanding to all this
Travis: in an agent having to be supervised by another agent,
Travis: you could see pretty quickly how this could get way out of control.
Travis: And that's that's where we find ourselves quite a bit.
Travis: Um
Travis: in
Travis: in the the healthcare space
Travis: I
Travis: mentioned s
Travis: ambient transcription is probably the most familiar, but it
Travis: really
Travis: I
Travis: 'd say
Travis: sort of to gain its traction in things like
Travis: Yeah, medical imaging was probably one of the first areas where AI really started to get into
Travis: the space of helping
Travis: s at least visually
Travis: better understand
Travis: um
Travis: what was being seen in a CT or an MRI or an X-ray.
Travis: Um
Travis: those kinds of things
Travis: were pretty prominent.
Travis: Um
Travis: clinical decision making kinds of stuff is is coming to be a big part.
Travis: Administrative functions, I mentioned the front desk
Travis: a huge part that's probably scaling um the quickest and probably the broadest
Travis: because it's it feels
Travis: safer
Travis: um and you don't maybe need the technology of some of that that really deep modeling that's tied to the the imagery kind of content
Travis: Uh
Travis: but we see it in
Travis: predictive
Travis: analytics
Travis: when it comes to deciding which of our panel members are the sickest.
Travis: And
Travis: when those are the ones that are identified, how do we manage them, right?
Travis: Do we go after them in a different way?
Travis: In the insurance space, maybe value-based contracting, uh, but in other spaces, maybe we say, hey, we need to bring you in for an
Travis: an you know
Travis: extra three sessions of acupuncture and massage therapy or make sure we're looking after your nutrition in a way that
Travis: that food as medicine becomes a a live and viable option here as we understand more and more about the science, those elements
Travis: are
Travis: part of AI.
Travis: And I think that's the really cool piece
Travis: is that
Travis: It
Travis: 's no longer just in the hands of
Travis: the large corporations who are harnessing
Travis: eight, nine
Travis: figure
Travis: budgets to fund AI.
Travis: Organizations now with relatively small budgets, let's say five, ten
Travis: , fifteen thousand dollars
Travis: can stand up incredible
Travis: capabilities to run an organization and Davin, your
Travis: your comments early on about
Travis: your ability to to build these things and help your patients in a way that
Travis: you know, ten
Travis: years ago you'd probably be like, what the heck are you talking about, right?
Travis: Um
Travis: it's very different now.
Travis: It's it's
Travis: um
Travis: all because of of AI and the capabilities that are out there.
Travis: It's really just about harnessing in the right way and protecting yourself
Travis: So that uh you don't get to the point where you've got inadvertent
Travis: problems, right?
Travis: Those are really expensive problems.
Travis: Um
Travis: I can tell you
Travis: more about those stuff pieces, those those elements certainly, but um
Travis: ,
Travis: for
Travis: changing.
Erik: I do want to
Erik: I do want to get into a little bit of that, but before I still want to
Erik: I want
Erik: to
Erik: Get a little more granular with some clarity aspect
Erik: because
Erik: I'm sure that there's others who maybe still are saying, I'm not getting this whole agent thing.
Erik: Right.
Erik: Now you just called superintendents.
Erik: And now it's like
Erik: So how
Erik: how
Erik: how does one
Erik: walk us through how one
Erik: creates an agent?
Erik: And
Erik: you
Erik: even said sometimes that there's agents that spawn agents or just uh
Erik: GPTs will
Erik: will
Erik: just
Erik: ca
Erik: already direct an agent to to conduct uh
Erik: a system.
Erik: So maybe maybe help us uh
Erik: because I, you know, I
Erik: Yeah, I'm visualizing, I think a lot of us do, right?
Erik: Star Trek and you have data, right?
Erik: You know, he's out there doing his thing and he's
Erik: he's, you know, this this ultimate AI
Erik: uh
Erik: being.
Erik: Um
Erik: so I think I
Erik: when you say agent, I'm immediately thinking, oh, Gata's out there doing some work for me or something, right?
Erik: So
Erik: tell
Erik: maybe, maybe
Erik: more specifically, let's get clarity
Erik: about
Erik: What's the
Erik: what's the difference between an agent
Erik: and the GPT in terms of if I'm just doing a prompt
Erik: How how does it go from just me giving it a task to now there's an agent that's running a whole system
Erik: Um, d
Erik: then
Erik: then is
Erik: creating other agents and then is deciding that it doesn't want to even follow the prompt.
Erik: I I I
Erik: I guess I'm I'm not and
Erik: I'm not clearly understanding
Erik: ha
Erik: that whole process.
Erik: And I think it's important
Erik: because in order for us to have
Erik: protection
Erik: and safety around that
Erik: Uh
Erik: I think we need to understand what it
Erik: what exactly these
Erik: components are.
Travis: Uh
Travis: so the
Travis: the difference between a GPT and an agent.
Travis: A great place to start
Travis: because the
Travis: the prompting, we've been
Travis: you know
Travis: hit over the head with all sorts of things, even the commercials, like just prompt your way to your new website, right?
Travis: You know, that's out there.
Travis: Just
Travis: a few sentences and you can make your own, you know, b
Travis: Hollywood movie, right?
Travis: It
Travis: does not work that way.
Travis: I don't know if you've tried it, but it doesn't, right?
Travis: Um, but here's
Travis: here's the difference between the two.
Travis: Um
Travis: we tend to
Travis: uh
Travis: express
Travis: the best prompting within the scope of three things.
Travis: what
Travis: 's the intent that you want to accomplish, what's the context that you want to operate within, and
Travis: what's the constraint
Travis: that it needs to understand.
Travis: And those three things together are really what
Travis: create a really good prompt, right?
Travis: There's other frameworks, but that's one that I typically use
Travis: And it is
Travis: it is not a fancy Google, right?
Travis: It is
Travis: not a a mechanism to just like
Travis: research, although GPTs are often used in that way.
Travis: They are
Travis: by nature
Travis: generative models
Travis: that tell you essentially what's the next best word and it puts all this context together and the constraints and
Travis: operates
Travis: within the scope of millions and millions, billions of data points and gives you an answer
Travis: based off of those three things
Travis: Uh
Travis: an agent on the other hand
Travis: is something that you provide the same kinds of uh
Travis: content to, but you provide a whole lot of other things.
Travis: So if I were to s to define it with respect to the robustness
Travis: A prompt might be two or three sentences.
Travis: A good one might be, you know, five or six, might be a paragraph long.
Travis: There's lots of ways to do this.
Travis: But
Travis: an agent might be built on, say, a five
Travis: page prompt, if you would say
Travis: if you can say it that way, because it defines
Travis: the scope, the
Travis: the authority that it has, the places it should go, the connections it should make.
Travis: And agents are are very prescriptive in the sense that you need to define one specific task.
Travis: So whereas a prompt might be a search for information, an agent does one thing.
Travis: You do not want it doing multiple things because it gets confused.
Travis: It's sort of like
Travis: um
Travis: a five-year-old with a PhD.
Travis: Right.
Travis: It it it will act like a child if you let it and it's so smart that if you put those two things together, you've got problems.
Travis: Hence
Travis: our organization.
Travis: But if you start pulling these things together, you can get to a better place.
Travis: So an agent
Travis: functions with one task and one task only.
Travis: And you define what those parameters are, what the guardrails are to that agent.
Travis: So you tell it where to go, what to do, how to respond, on what frequency it should respond, how it should consider things, what tone it should use.
Travis: and an agent
Travis: functions within those guardrails.
Travis: Now, the interesting extension of that is the agentic
Travis: AI space.
Travis: That's the space where it truly has autonomy
Travis: to not only do those things within those guardrails.
Travis: But to then start making decisions about what to do next.
Travis: And if you stop and say, here's what I'd like you to do, I'd like you to go
Travis: and assess for all of the current records that are in my EHR
Travis: and give me the top 10 patients that are going to the ED most frequently so I can reach out to them.
Travis: And ensure that we put them on a special white glove list.
Travis: Right.
Travis: It can go out and answer that within the garbers.
Travis: The next level of that is that an agent has the capability to make a decision of
Travis: What do I do next?
Travis: Right.
Travis: And it can do things like, well, let me split out the information, put them in the special protocol batches, and then let me make a couple
Travis: outreach phone calls or SMS messages to define
Travis: um
Travis: what they should be doing and
Travis: how they should be outreaching back to the clinic.
Travis: And let me make sure that I'm also assessing for the clinic's ROI on that outreach.
Travis: How much marketing is is the clinic spending on
Travis: developing relationships with those clients.
Travis: Are they membership paying clients or are they not membership?
Travis: And it starts to ask questions and act on its decisions.
Travis: Then you go backwards.
Travis: Let me go back to the agents that
Travis: acts in the guardrails
Travis: that doesn't do that.
Travis: It just says, let me go and get this information.
Travis: And then back one farther, the GPT prompt that you put in.
Travis: that does nothing besides
Travis: just give you an answer
Travis: within the
Travis: the
Travis: intent, the context and the constraint of that prompt.
Travis: And you could see now the bookends of
Travis: here's an answer I'm giving you
Travis: versus
Travis: I'm now making decisions
Travis: as an agent based on what's coming back to me.
Travis: And I'm gonna act until you tell me not to.
Erik: Mm-hmm.
Travis: And if you don't tell me to stop, I'm going to be that agent that just destroyed that company's data set in nine seconds.
Travis: Because
Travis: That's what I
Travis: that's what I'm allowed to do.
Travis: And if you don't give it those guardrails, it'll continue to act.
Travis: Well I'm not sure if I answered your question.
Erik: Okay.
Erik: Essentially an agent
Erik: is a
Erik: program
Erik: that
Erik: you
Erik: create guardrails for
Erik: but allow it to make
Erik: decisions, right?
Erik: It is
Erik: it is a decision-making program
Erik: that you set
Erik: up
Erik: Where
Erik: when you just do a prompt, it's a you're just getting an answer.
Erik: You're asking a question, you're getting an answer.
Erik: Where an agent is gonna be a program
Erik: that has decision-making
Erik: power.
Erik: to
Erik: provide
Erik: a system
Erik: of
Erik: information
Erik: um
Erik: that you
Erik: set guardrails
Erik: to
Erik: So I think that and
Erik: that that then makes sense
Erik: to accomplish some
Davin: or to accomplish some task.
Erik: Right.
Erik: Right.
Davin: That you've given it
Erik: But you've given it you've given it some parameters
Erik: and it has decision-making ability to
Erik: decide what
Erik: information to pull
Erik: and and how to
Erik: organize it
Erik: and present it.
Erik: Um
Erik: but in
Erik: it it has some
Erik: decision making
Erik: and I think that's the key component, right?
Erik: The agent has decision
Erik: making power.
Erik: And
Erik: where the where
Erik: the
Erik: GPT prompt
Erik: is just going to answer your question.
Erik: Did I understand that correctly?
Travis: For our conversation, you nailed it.
Travis: But
Travis: um
Travis: the
Travis: the decision making piece, uh
Travis: sort of the
Travis: the swing into the compliance and governance, the protection spot.
Travis: If you
Travis: if you give your
Travis: your agents
Travis: the capability and authority to do something and it does, you have to be able to answer to that.
Travis: Right?
Travis: You are the deploying organization of that agent.
Travis: It's not the developer.
Travis: It's not the architect of the agent.
Travis: So
Travis: if a client
Travis: deploys an agent
Travis: And the agent does something.
Travis: The regulators are going to come back to the deploying organization and say, hey, what do you guys think about this?
Travis: Prove to me that what it did
Travis: Not what the policy said it was supposed to do, right?
Travis: Not what the SOP or the governance
Travis: standard is.
Travis: You tell me what it did and how did it make its decision, then unless an organization can do that
Travis: You're going to be in trouble, right?
Travis: And that's where the rules and laws and in
Travis: our standards are going.
Travis: And here in the US we have
Travis: Um
Travis: we have no AI act that's in play.
Travis: We have governance standards.
Travis: We have compliance standards.
Travis: Um
Travis: other
Travis: organizations
Travis: do.
Travis: Um
Travis: the EU
Travis: is rolling out with a a relatively strong one.
Travis: China has one.
Travis: But when we think about how we define that through legislation, there's a
Travis: there's a very clear position taken by the current administration, which is unique to previous administrations
Travis: that
Travis: we should be able to just sort of all
Travis: allow the innovation to occur.
Travis: Prior administrations had on the books very different kinds of standards.
Travis: And
Travis: as the administration's changed, it's shifted the
Travis: the space of AI and how we define it
Travis: and
Travis: organizations that
Travis: are
Travis: pushing for the definitions to have consistent definitions, um, are
Travis: really
Travis: trying to say
Travis: this shouldn't be a
Travis: an agent defining itself.
Travis: It shouldn't be independent.
Travis: I shouldn't be able to just ask you
Travis: Erik, Davin, did you guys actually follow the rules?
Travis: And you say yes.
Travis: And I say, okay.
Travis: Right.
Travis: That's
Travis: that's a that's a
Travis: that's a self-verification, right?
Travis: It means really nothing to auditors
Travis: Second, independent.
Travis: It's
Travis: hey
Travis: Travis, did Erik and David,
Travis: sorry, Davin, release this agent
Travis: and did it do what I was supposed to do?
Travis: And I might say, let me look at the books, lift up the hood, check it.
Travis: Cool
Travis: Then there's a cryptographic.
Travis: And outside of, you know, being sitting in front of people who really want to know that kind of stuff, that's just the highest level.
Travis: It's not even fully defined.
Travis: It's most certainly not within
Travis: the
Travis: true legislation, you know, executive powers of our governments right now who would say this is how it's defined.
Travis: But you can think of things like
Travis: It has to be interoperable, it has to fit regardless of the system, it has to tell you every single step, it has to be independently verifiable by machines, not by humans.
Travis: All these different standards that go into this kind of standard of governance
Travis: That's the kind of AI that we're moving toward.
Travis: And
Travis: we're gonna get there quick.
Travis: Um
Travis: I I do feel like there's some administration stuff that's going to push it one way or the other, especially as we move into the next couple of years, but
Travis: You know, i
Travis: the AI space, especially in healthcare, because it's so heavily regulated, it is a space to absolutely be
Travis: um
Travis: aware of the fact that just because there's not an AI act, a single thing that tells you
Travis: Don't make the assumption that you're safe, right?
Travis: Because the EEOC has their thing, right?
Travis: FTC has their thing.
Travis: Every
Travis: every
Travis: lettered governmental agency has
Travis: their standard that they're living by right now for AI.
Travis: And depending on which one of them you want to talk to, the definition changes.
Travis: And there's no unified thing.
Travis: So that's where we're we're sort of in our space right now.
Travis: The big takeaway, and then I'll stop because I'm on a I'm on a
Travis: I'm on a
Travis: bad pedestal right now or soapbox.
Travis: Um
Travis: be careful, right?
Travis: It's a it's a moment of me saying on a risk side
Travis: Do it, be innovative, press forward, incredibly powerful, use it to your heart's content, but don't
Travis: don't risk the point that because there's no one single law that tells you not to, that that's the way that it's gonna be
Travis: Build it with uh a conservativeness into your system
Travis: and always have a way to ensure that you can track what's being done and if nothing else, ensure that you can stop stuff before it goes
Travis: if it contains
Travis: PII protect
Travis: or personal identifiable information or protected health information.
Travis: Um, and that's that's the name of the game right now.
Travis: Just push, but uh
Travis: make sure that you
Travis: got some protection.
Davin: That's good.
Davin: I
Davin: I really like that.
Davin: I think um, you know, i
Davin: innovation
Davin: uh
Davin: is is so amazing.
Davin: You know, I think in healthcare
Davin: for years
Davin: uh
Davin: this was the challenge though, right?
Davin: Is the
Davin: um
Davin: the privacy, the security, the the level of
Davin: um
Davin: uh
Davin: you know, the kind of data that you're dealing with, right?
Davin: It's not a it's not a dinner reservation, you know.
Davin: So
Davin: um, you know, your ability to schedule that, you know, is
Davin: is so different, right?
Davin: And I think that's why it's taken so long for technology
Davin: that we see everywhere else in our in our ecosystem
Davin: to hit healthcare, right?
Davin: Because of the complexity and the sensitive nature of of the data, right?
Davin: And then also
Davin: all these, you know, systems that are disparate
Davin: and use different standards and and and whatnot
Davin: as well, right?
Davin: Um
Davin: and so
Davin: uh
Davin: yeah
Davin: I think it's it's it's an
Davin: it's gonna be interesting to kind of see
Davin: how
Davin: AI, um, also, you know, i
Davin: interacts with with healthcare.
Davin: And to your point, there's a lot of people using it
Davin: not
Davin: officially
Davin: under
Davin: the guise of of a setup like yours, right?
Davin: Um
Davin: and and they're doing it innocently, not with bad intentions, but
Davin: you know, to try to do the right thing, to try to
Davin: be more effective at their job to help more people, right?
Davin: I mean, I think that's been my experience in healthcare
Davin: is you have lots of people with great intentions, um, but their solutions don't all
Davin: uh
Davin: aren't always thought through in a way
Davin: um, you know,
Davin: that aligns with
Davin: those good intentions.
Davin: Yeah.
Davin: And there's unintended consequences that happen in other things.
Davin: And so
Davin: um
Davin: if people
Davin: wanted to, I mean, your
Davin: company
Davin: um
Davin: sounds, you know, like a a really good option for people.
Davin: Is it designed for large enterprises?
Davin: Is it designed for
Davin: individuals, business, small businesses, small practices.
Davin: Yeah, help us understand that.
Davin: And then
Davin: um
Davin: and then
Davin: if it's an option for our, you know, users
Davin: uh
Davin: viewers to to take advantage of, you know, make sure we know
Davin: they know how to do that.
Erik: Yeah.
Erik: And then I wanna I wanna give you two scenarios
Erik: because
Erik: you share
Erik: answer Davin's question, but I want to give you two scenarios
Erik: then
Erik: And maybe then you can tell us how, you know, whether or not those are safe
Erik: exercises, activities
Erik: And w
Erik: and how
Erik: what you guys are doing would maybe help protect against bad outcomes within those scenarios.
Erik: Okay.
Travis: Okay.
Travis: Uh
Travis: so
Travis: to hit on your your question, Devin
Travis: Um
Travis: our
Travis: primary focus is
Travis: really smaller organizations.
Travis: When I say smaller, I mean like
Travis: less than 500 staff.
Travis: Um
Travis: we
Travis: the idea that we're not having conversations with
Travis: um you know, the
Travis: the Mount Sinai's of the world.
Travis: The
Travis: the reason behind that
Travis: is because most of those large organizations are throwing their millions of dollars at it
Travis: Right.
Travis: They
Travis: they
Travis: have teams to do these kinds of things and they're thinking about it and they're protecting themselves with
Travis: uh
Travis: boardrooms of attorneys and compliance officers.
Travis: where
Travis: we're at is there are a lot of things that happen in smaller organizations, clinical practices
Travis: that need
Travis: that support.
Travis: That should be just as protected
Travis: at a price point that makes a lot of sense for them.
Travis: And that's an important space for op
Travis: for us to operate in.
Travis: Our
Travis: Our tools are agnostic.
Travis: They can
Travis: uh
Travis: they
Travis: can allow for hundreds of agents to be bounced up against it at any given time, with very little lag, if any at all, talking 0.
Travis: 06 milliseconds, like tiny amounts of time
Travis: Nobody would experience that slowness.
Travis: That
Travis: space, because of its ability to manage it, it can be deployed in the largest of organizations.
Travis: But that's that's not our focus.
Travis: If a health system said, hey Travis, we'd love to use you guys.
Travis: Okay, that's great.
Travis: Our priority is
Travis: organizations that we know are pushing forward, striving to be innovative, continue to stay up with what's currently occurring, and make no mistake about it.
Travis: Every single person that either uses and stores your medical records or pays you because of the work that you do
Travis: is using AI.
Travis: for their purposes, right?
Travis: So
Travis: remembering that there's a very clear other side of the table here that that
Travis: as smaller organizations are seeking to to stay up and stay protected.
Travis: They also have to be aware of the fact that the other side is using that information in ways that support their missions
Travis: and it may not always follow yours.
Travis: You need some capabilities to ensure that you have agents to act quickly, act autonomously
Travis: And you need to be able to have that same space be protected
Travis: as it does that.
Travis: So
Travis: our focus is on the smaller groups, just to give you that.
Travis: I feel like I'm ready for the the Jeopardy round here.
Travis: I'm ready for your examples.
Travis: Let's see how we do.
Erik: All right.
Erik: So here's the two scenarios.
Erik: So let's
Erik: the scenario
Erik: the first scenario would be a provider
Erik: who is interested in getting some
Erik: a different perspective, maybe a little more information about a patient they're caring for.
Erik: They're in the visit with the patient.
Erik: and
Erik: they then
Erik: put information
Erik: of that patient into, say, let's say
Erik: chat
Erik: GTP.
Erik: Um
Erik: but it's
Erik: it's not protected.
Erik: You're just putting the information in there.
Erik: Um
Erik: and
Erik: and let's say they upload
Erik: uh
Erik: labs into that because they want to get s a different perspective on how to interpret the labs.
Erik: Um
Erik: is
Erik: that
Erik: a HIPAA
Erik: violation?
Erik: Is
Erik: that
Erik: a
Erik: a
Erik: is
Erik: what ends up happening with that
Erik: information
Erik: and who has
Erik: who has the capability of accessing that through a
Erik: chat GPT?
Travis: So
Travis: uh
Travis: yes to the first part, which is
Travis: is it a violation?
Travis: Yes.
Travis: Um
Travis: it is not a fineable violation until somebody says that it's a violation, right?
Travis: So
Travis: I can say yes, but you know, if no one knows, no one's the wiser, if I can put it that way.
Travis: Yes.
Travis: Um
Travis: the idea that
Travis: um
Travis: content
Travis: that gets into that space
Travis: and
Travis: um
Travis: needs to be accessible
Travis: or how could it be accessible?
Travis: I think was sort of the the tail end of the conversation or the question.
Travis: Once it gets there, who can access that content?
Travis: It's not a space that any of us can just tap into the GPT model and get it.
Travis: But here's the interesting part of this.
Travis: One, um, if you're using a GPT,
Travis: uh any
Travis: GPT that's out there and you're just using it in the open space, you don't have a membership or a you know
Travis: paid subscription for a pro
Travis: model that's a little more secure, you just you know
Travis: Chat
Travis: GPT.
Travis: com anywhere as you
Travis: will.
Travis: That is pure open source.
Travis: Which means the
Travis: the
Travis: actual
Travis: content that's submitted to the large language models of that engine
Travis: is usable to train the engine itself.
Travis: Now, in and of itself doesn't sound like a bad thing, but what happens if you start continuing to do that and you do that over and over again?
Travis: Nothing's really happening until you start actually asking questions of it.
Travis: You're training the model to do very specific things.
Travis: And if it understands that you keep doing this, as it starts to respond, it's going to understand that you may be thinking more about that.
Travis: It creates things like biases
Travis: and its
Travis: responses.
Travis: It can be used publicly to find stuff.
Travis: And that's where
Travis: if a really smart individual were to start accessing
Travis: information, you can query
Travis: prompt
Travis: a GPT from outside of your clinic, knowing what your clinic does, the name of your clinic, potentially even patients, right?
Travis: And
Travis: can ask or prompt GPT to query content that's in the trained model.
Travis: It can actually, through prompting only, pull content out, essentially coax the GPT responses to present information back that is absolutely PHI or PII from stuff that you've loaded up.
Travis: So
Travis: because that content goes into a public space, what has to come out eventually is that same data.
Travis: It's mixed in with all sorts of stuff, but
Travis: There's
Travis: because
Travis: you can, you know, develop agents that will go out and
Travis: call the data and scrape stuff and query over and over and over again, there's ways to find that information if
Travis: somebody's really motivated to do that.
Travis: So
Travis: yes, it's a hippo
Travis: violation.
Travis: No, you don't want to load up things.
Travis: Can it impact the biasness of the response?
Travis: Yes.
Travis: Last.
Travis: the individuals who can
Travis: especially in open source content can go out there and find it.
Travis: You can eventually get it to respond
Travis: with data if you have enough ties.
Travis: um
Travis: or specificity about the data you want, you can definitely scrape that back out of the system.
Erik: So before I go into
Erik: to scenario two, then
Erik: would your
Erik: does your company act as a filter to help
Erik: Like
Erik: if I wanted to do that, I want to use the
Erik: the GPT
Erik: and I let's say I ha I have a membership or I
Erik: paid, you know, so it's a little more secure, but still i
Erik: is there
Erik: Is there a way to have a layer of protection so that I'm not getting myself into trouble just because I'm curious and I'm asking questions?
Erik: I'm trying to get information.
Erik: I'm not really thinking.
Erik: about
Erik: the
Erik: broad implications
Erik: of what I am I'm putting into this database
Travis: We have
Travis: agents
Travis: that
Travis: enforce the point of action.
Travis: So as soon as you hit the go button on
Travis: that prompt
Travis: or that agent going out to do something, the agent will go out to the
Travis: to the large language model itself.
Travis: It can bypass the the front-end
Travis: prompting that you might have.
Travis: It's at that point that it will determine based on whatever agent we put in between
Travis: there.
Travis: Does this make
Travis: does this matter to your SLPs?
Travis: Does this matter to your policies?
Travis: Does this matter to your internal documents?
Travis: That
Travis: sort of company compliance is the first toll gate that we that we have at all agents.
Travis: Second one is
Travis: does it release PAI, PHI, does it violate high trust or HIPAA
Travis: concerns
Travis: As you get further into those different compliance packs, it absolutely stops it, renders
Travis: one of those three verdicts
Travis: of
Travis: do I pass it, do I pause there, do I prevent it?
Travis: And then drops into that auto chain to escalate it out to whatever regulator you might have.
Travis: So
Travis: if somebody in this case were to say, hey
Travis: agent, go out there and and
Travis: each Monday
Travis: go and find me all of this content, right?
Travis: It will absolutely act at that point and say, nope, this Monday you guys tried to do something, it wasn't correct, or you had a front desk person try to load up all of this.
Travis: um
Travis: patient record contents.
Travis: I
Travis: they
Travis: probably wouldn't do that.
Travis: Maybe
Travis: load up a schedule that they printed.
Travis: So they downloaded a schedule for the last month and said, I need to do a better job of organizing the schedule because of
Travis: next month so many people are going to be out.
Travis: Help me out with this GPT.
Travis: In that scheduler, probably a bunch of patient names, right?
Travis: Maybe dates of birth, some other identifiable information to help link it up to medical records, possibly.
Travis: Um
Travis: if that were to be the case, those agents would say, mm-mm, like we gotta you gotta stop this.
Travis: If there's content that it will
Travis: that it can pass through, great.
Travis: The content that it shouldn't
Travis: It'll stop it a wholly, it'll uh redact it,
Travis: scrape it out, or it'll tokenize it if we need to do some sort of other fancier stuff to make sure that the data can move into other spaces and be used.
Travis: But
Travis: absolutely stops it.
Travis: So
Travis: that's
Travis: that's the target
Travis: you've got to pass
Travis: is
Travis: those agents that say, mm-mm,
Travis: thank you
Travis: Like a hall monitor, you know, of school, right?
Travis: Either
Travis: either you have the key with a two
Travis: by four attached to it for the bathroom, right?
Travis: That you have to get permission to walk in the hall with, or you don't.
Travis: So
Travis: you either get to walk down the halls or you or you don't or you get pushed back and say, Hey, you better go get the, you know, the wooden stick with the bathroom key on it
Travis: because uh you're not passing me.
Travis: And that's
Travis: That's the hall
Travis: monitor that we'll that we're really talking about.
Erik: Awesome.
Erik: Okay, here's scenario two.
Erik: Okay.
Erik: All right.
Erik: So
Erik: uh
Erik: my clinic, we have multiple providers
Erik: and a provider
Erik: decides that they want to collect data
Erik: um
Erik: across the board.
Erik: They want to know how many
Erik: how many of our patients
Erik: Their A one
Erik: C's are greater than seven percent, and they want to know how many of those we have in our practice.
Erik: Can
Erik: they use
Erik: a GPT
Erik: to access our EMR
Erik: to be able to comb through the data and be able to find that within
Erik: our
Erik: system
Erik: or is
Erik: the EMR kind of protected against a GPT
Erik: entering
Erik: into that space
Erik: and combing that information?
Travis: Um
Travis: so
Travis: you can have it piped directly into your EMR.
Travis: That
Travis: space is getting tighter and tighter, as I mentioned at the beginning.
Travis: They're sort of cutting those portholes off and saying, hey, we're doing this.
Travis: But
Travis: where
Travis: where
Travis: they'll allow it, yes.
Travis: Um
Travis: I'll connect a comment, Devin, that you made a little bit earlier around the skills.
Travis: So I'll reference Claude just to be specific.
Travis: So Claude
Travis: has skills
Travis: It has cowork, which is one of the three areas, chat co-work and cloud code, that it can use to help make things easier for people.
Travis: If you, um, Erik
Travis: said, um, I'm going to create a
Travis: a skill
Travis: that tells my system to go into my EHR and grab this particular data set
Travis: and then throw it into
Travis: a a GPT model of some sort.
Travis: Those kinds of integrations are absolutely possible.
Travis: They absolutely happen and they are very, very effective at what you do.
Travis: So
Travis: a as a
Travis: maybe kind of stepping from healthcare for a second, if you just said
Travis: I'd like to have my
Travis: my GPT connect with my email
Travis: and then connect to my
Travis: um
Travis: CRM so I can send emails directly out to my patients.
Travis: Those integrations that you see companies standing up and they say, hey, let me just connect all the pipes together, those are essentially the same kinds of things that skills do on an automated and a much bigger platform.
Travis: So they just sort of say, hey, what do you want me to connect to?
Travis: You want me to connect to your AHR?
Travis: That's great.
Travis: Let me go get permission from uh
Travis: Athena
Travis: and Cerner
Travis: and
Travis: name all those epics, right?
Travis: And then let me make sure I connect you up with my Hubspots or my custom CRM.
Travis: And I'm going to go in, you know, email these folks,
Travis: very
Travis: possible.
Travis: Now, the question whether it's protected is a different question.
Travis: Um, that's where you would say
Travis: if things move between spaces, most of those pipes are as protected as they can be.
Travis: But
Travis: you have to get
Travis: permission to allow it to happen.
Travis: So if you allow for
Travis: the connection between your EHR
Travis: to the GPT
Travis: to a SMS service that's going to send a text message to people.
Travis: That
Travis: connection is your
Travis: responsibility
Travis: and likely a violation of all sorts of rules
Travis: because
Travis: you might not have business associated agreements with those various organizations to protect
Travis: the data that's that's moving
Travis: between those systems.
Travis: So
Travis: as soon as that happens and it moves into a space that you don't have a BAA
Travis: with.
Travis: Now all of a sudden you've released to them protected information that they don't have permission to have, even if it's
Travis: something as benign as a name and an email to send us something out.
Travis: So can you
Travis: absolutely
Travis: how
Travis: how would our organization manage that?
Travis: We would have agents that would sit inside of your
Travis: skill
Travis: That would say
Travis: if you launch this, then here's as far as you're willing to go.
Travis: As soon as it comes back and touches a GPT
Travis: We say
Travis: stop
Travis: at that moment.
Travis: As soon as you develop a connection to another space, you may be able to get around it, right?
Travis: So it's a
Travis: it's
Travis: it's important for us to be able to
Travis: um
Travis: be within what we call the runtime space, the activity that happens for your agents.
Travis: And when you integrate our code into that
Travis: Then it knows.
Travis: I can't go further than this because you're about to stop me.
Travis: And the agents during the run of the process
Travis: will say, uh
Travis: oh, I
Travis: I just hit something that's not going to be acceptable and that stops it.
Travis: um
Travis: drops it down to that tamper
Travis: tamper proof audit chain and says, here's what's happening.
Travis: So
Travis: the cool thing is
Travis: you can get, you know
Travis: Carragon or a make.
Travis: com
Travis: or a zapier
Travis: to connect all sorts of pieces.
Travis: Um
Travis: the difference really quickly between a make.
Travis: com and a zapier
Travis: that's sitting
Travis: or a carragon, excuse me.
Travis: Carragon does have business associated agreements.
Travis: So
Travis: it knows
Travis: that when you want to connect things, it needs to have those agreements in play for all of its pathways.
Travis: And it does a wonderful job of that.
Travis: make
Travis: more robust, but it doesn't have BAAs.
Travis: Zapier
Travis: definitely doesn't have BAAs in those spaces.
Travis: So when you think about the protected health information
Travis: And you want to connect stuff, you just have to be aware of which organizations you're using to help connect the pieces, and then you're a little bit better positioned.
Travis: But
Travis: um
Travis: Boy, that was a long answer to simply saying yes, you can connect it, but you're about to get into trouble if you're not careful.
Travis: That's
Travis: I keep going back to the same thing.
Travis: There's always ways to protect yourself, but the cool thing about AI is that almost anything is possible.
Erik: Yeah, absolutely.
Erik: Well, that was
Erik: that was great, Travis.
Erik: I think that was really helpful
Erik: in
Erik: we don't really
Erik: often think
Erik: about
Erik: the impact that AI is having
Erik: Sometimes in the background
Erik: or, you know, in in our systems, we just
Erik: think about, oh, EI makes my life easier because I can
Erik: uh
Erik: write a document quickly, I can search for some data points, I can
Erik: I can
Erik: access and put together
Erik: collate something quickly.
Erik: Um
Erik: much
Erik: like uh you know the early printers and and fax machines allowed us to start communicating and organizing things in our office, I think we
Erik: a AI is just doing that at a trillion times faster
Erik: and
Erik: uh
Erik: but the complexity of it, the risk associated with it
Erik: is a real
Erik: uh uh
Erik: entity that we have to really think about and and process.
Erik: So thank you for coming on the podcast and sharing
Erik: Does Davin
Erik: any
Erik: uh
Erik: final
Erik: questions before we close up today?
Davin: Well, I think you know
Davin: something we ask uh a lot of our guests
Davin: is
Davin: um
Davin: where they kind of see the future of
Davin: of healthcare going, um
Davin: kind of through their own lens, right?
Davin: And um
Davin: you know, if you if you were to sort of
Davin: take your lens and and where healthcare's headed in terms of AI
Davin: and and what it might do
Davin: for
Davin: uh
Davin: for this
Davin: even just the science of health or the technology of healthcare or the
Davin: Delivery of healthcare, um
Davin: any
Davin: big predictions you want to make?
Travis: Um
Travis: you know, I
Travis: so I might have a I might have a a couple.
Travis: Um, the first one is
Travis: the
Travis: agents
Travis: are very good at giving you back choice, right?
Travis: The choice is whether or not you want to use the time
Travis: to
Travis: take care of yourself
Travis: to have a relaxing afternoon, to give your front staff a little bit of extra time, an hour off because they've gained back the time and their efficiency.
Travis: Or if you want to use that choice to just fill it with more patience, more work to get done, make more revenue, whatever that might be.
Travis: So
Travis: I I've stopped kind of saying AI replaces people
Travis: because it doesn't always replace people.
Travis: Sometimes it just gives you the choice.
Travis: It always gives you the choice because it does create that capability.
Travis: Um that's just a very blanket
Travis: sort of AI
Travis: comment.
Travis: Um
Travis: the other space I would say
Travis: um
Travis: it
Travis: it is going to
Travis: advance clinical work in a way that um
Travis: is
Travis: sort of befuddling to me.
Travis: Can't predict what that's gonna look like, but I would say that every time I have a conversation with
Travis: folks in the pharmaceutical space or the clinical trial space, the capabilities of AI and the models that can contribute to the speed at which new treatments come about
Travis: is
Travis: powerful
Travis: um
Travis: on a personal basis.
Travis: I'm not a big medicine fan.
Travis: I'm very much a an individual that loves food as medicine as a concept.
Travis: Expand it from there.
Travis: And so
Travis: what I would love to have happen is actually for
Travis: AI organizations
Travis: to stand
Travis: up capabilities to advance the science of how
Travis: how food and nutrition
Travis: and
Travis: Better health, how you take care of your body
Travis: actually impacts your future health.
Travis: And less about whether or not we can develop the fastest.
Travis: you know
Travis: research
Travis: to
Travis: pill dispensary
Travis: way to cure some illness, right?
Travis: And that's that is a
Travis: a
Travis: thing that I really believe AI
Travis: should be tackling.
Travis: I I don't want AI to tackle the solve the problem
Travis: after it's happened, the sick care
Travis: space.
Travis: It needs to solve the how do we just be healthier humans.
Travis: And
Travis: Part of our organization is is protecting human data from badly behaving agents.
Travis: I think AI
Travis: should be one of protecting human sickness and protecting human health so that we end up in a place where sickness doesn't even come in until you just fall off the cliff and die.
Travis: Like I I don't know if I've mentioned that before, but that's like
Travis: it's my thing.
Travis: I want to climb Mount Everest and then fall off the other side and just die.
Travis: Like I don't I don't want to slowly just like
Travis: wither away.
Travis: You know, I want to
Travis: I want to live until I'm just not healthy enough to live and then
Travis: and then that
Travis: my that's my day.
Travis: So those are
Travis: that's a couple predictions, a couple statements, and a couple wishes
Travis: maybe.
Travis: Perfect.
Travis: I like it.
Erik: Well, thanks, Travis.
Erik: It's been great.
Erik: We really appreciate you having on
Erik: you on the podcast today.
Erik: And we look forward to hearing what you guys think about uh what was shared today.
Erik: So please leave any comments
Erik: if you are so inclined and we look forward to having further conversations about AI in the future.
Erik: Yeah.
Erik: Thank you, gentlemen.