Rewriting the Clinical Playbook: Dr. Shiv Rao on Scaling Empathy with AI

Dr. Shiv Rao, cardiologist and CEO of Abridge, joins hosts Raj Manrai and Andy Beam on NEJM AI Grand Rounds for an inspiring conversation at the intersection of medicine, technology, and meaning. Shiv shares the origin story of Abridge, reflecting on how a deeply human encounter in clinic sparked the idea for a company now transforming clinical documentation across more than 100 health systems. From his early days programming electronic music to navigating LLM deployment at scale, Shiv offers a rare look into the soul of a founder building not just infrastructure — but a movement. He unpacks how generative AI can be used to restore presence in the clinic, what it takes to earn clinician trust, and why he believes taste, empathy, and curiosity are the real moats in health care AI.

Transcript.

Episode Transcript

(00:01):
I saw this patient.
She had a 10-year history of breastcancer. And she was coming to see me
because she needed preoperative cardiacevaluation prior to starting doxorubicin,
and she was super nervous and anxious.
So, at the end of the encounter, I askedher why and if there was something
I did or something I said to makeher clearly feel so uncomfortable.
And she told me that since shewas diagnosed with breast cancer,

(00:24):
her husband had come down to
every single visit with a new type ofdoctor, but he couldn't make this visit.
Him coming to those visits just helpedher feel more present and I said, why?
What does he do that's not obvious?
And she said that he's quiet.
He just, he's in the corner.
He just, he just takes notes.
Him taking notes meant that she feltlike she could make eye contact with me.
She could feel more presentknowing that they could go home

(00:47):
and rewrite all those notes. Googleall the big words and then go
to the next clinician and feellike the main characters of their
story retelling it as opposed tosomeone looking in from the outside.
And I think the revelation for meand for so many of us, it's all
about all of us really wantingto build better relationships,
and have better conversationsand be, be better to each other.

(01:07):
And so, wouldn't it be paradoxicallyprofound if we could somehow
leverage technology to bringthose people closer together?
Welcome to another episodeof NEJM AI Grand Rounds.
I'm Raj Manrai.
I'm here with my co-host Andy Beam,and today we are delighted to bring
you our conversation with Dr. Shiv Rao.

(01:29):
Shiv is the founder and CEO of Abridge.
Abridge is a health care AIcompany that's creating software
for clinical conversations.
And during our conversation, wereally got to dig into why Shiv
started Abridge, what they're tryingto do, and what traction they have.
And I'd also say, Andy, thisis a pretty wide-ranging
conversation, even more than normal.
Shiv is a pretty cool guy, andhe has done some electronic

(01:52):
music, lots of other things,
I think maybe we talked aboutskateboarding at one point.
He has a lot of interests outsideof medicine and health care and AI.
So this was really fun.
Yeah, totally agree.
And you know, we first heardabout Abridge through a friend
of the show, Morgan Cheatham.
We were at
the NEJM AI event in Puerto Rico,and he gave us a demo, and we were
both kind of like blown away.

(02:12):
So, Shiv is an amazing CEO.
You can tell that he's thought alot about his company's mission.
He can tell it very succinctlyand when you hear him talk about
it, it's tough not to get excited.
I think one of the reasons I was sointerested in this conversation is that
the revolution of AI and health carehas been happening for a long time now,
but we have yet to see very durable andsustainable business models behind that.

(02:35):
And Abridge is really at theforefront of having not only compelling
technology that doctors franklylike, you know, this is not a paid
advertisement. But it does parts oftheir job that they just don't enjoy
and it does that part well.
But they also seem to havetraction commercially.
So, it's been very interesting tosee this new wave of AI companies
that both have interesting tech butalso interesting business models.
And so it's really great to diginto Shiv on both of those topics.

(03:00):
The NEJM AI Grand Rounds podcast isbrought to you by Microsoft, Viz.ai,
Lyric, and Elevance Health.
We thank them for their support.
And with that, we bring youour conversation with Shiv Rao.
Shiv, thanks so much forjoining us on AI Grand Rounds.

(03:21):
We're excited to have you here.
Oh, I'm super stoked to be here,
thank you. Shiv,
great to have you on AI Grand Rounds.
So, this is a question thatwe always get started with.
Could you please tell usabout the training procedure
for your own neural network?
What data and experiences ledyou to where you are today?
Yeah, I love that framing.
Maybe I'll go backwardsand then go forwards again.
And I think there, there's just like afew stories that are imprinted inside

(03:45):
me, in my brain, that I think continue tobe really informative and influential.
So,
prior to starting this company calledAbridge in 2018, I was an executive
at a large health system called UPMC.
I sort of violated Peter's principle10 times in five years, but I got
to be the check writer for theircorporate venture capital portfolio.
We were deploying their moneyinto startups, but also into

(04:06):
Carnegie Mellon University.
We started a machinelearning and health program.
And I got to learn osmotically fromthose professors and those Ph.D.'s,
those postdocs that we were funding,and it was just an amazing privilege.
A couple lifetimes ago as a collegestudent, I went to Carnegie Mellon
University, and I think I was likesubconsciously rebelling against
all the Indian doctors in the world.

(04:26):
You know, against my dad,against my sister, against
everybody I knew and grew up with.
So, I studied a lot of the other stuff.
I was in the negative space, ifyou will, of what I'm doing now.
I got a scholarship year to programvirtual synthesizers in the school of
Computer Science and School of Fine Arts.
Took a lot of film theory and art theory.
I think I have a tendency to getreally, really obsessive about

(04:48):
something and then get obsessive abouta seemingly like disparate thing.
And when I was a junior incollege, maybe this is like a
first story that's informative,
I met this professor, this architectureprofessor named William McDonough.
He was visiting and he was giving alecture about sustainable architecture.
But really, like he was talking aboutdesign thinking when I think the

(05:10):
d.school at Stanford was still prettyyoung, and he told this story about this
ophthalmologist in India who designedthis revolving platform that he sits
on, and he used to do cataracts, butthe way he would do it, he was, he'd sit
on the platform, they'd bring a patientin at 12, he'd do a cataract, he'd
say, spin, then do another cataract atthree, spin five minutes later, cataract

(05:31):
at six, and then spin, spin, spin.
He's like spinning allday long doing these.
Five to 10 minute procedures, and by thetime I'd heard this lecture, he'd given
eyesight to over a million people, and hetaught the procedure to his daughter who'd
given eyesight to over 400,000 people.
And I remember thinkingbiblical-like impact.
This is, this is like next-levelimpact that he had delivered

(05:51):
to the world. I remember like leavingthat lecture thinking, you know,
maybe I wanted to be in health care.
So, I pivoted pretty hard.
I spent about a year making music andplaying music at, like, art museums.
It was like pretty weird music andlike kind of avant garde electronic
stuff where you just sort of like,you don't dance, and you just like,

(06:12):
close your eyes and, like, listen to it.
It used to be called intelligent dancemusic, like IDM, or Autechre or Aphex Twin, if
you're familiar with those sorts of folks.
I, I, I think we should, not tointerrupt you, I'm really enjoying this
shift, but yeah, I feel like we shouldtry to splice some of that into this
episode if we can at, at some point.
So, great to, great to, great tohear, uh, here's some of your work.
I finally like figured out how to, in that scholarship year that I

(06:35):
got at Carnegie Mellon, I got to dopre-med requirements and so like,
kind of pivoted pretty late, wentto med school, went to residency.
And then kind of found my passionand I think like that passion is
absolutely at this intersectionof like health care and technology
where I perceive the most impact.
I think it's like back to thatophthalmologist story of, like,
how do you create that kind ofimpact at that kind of scale?

(06:56):
And it's not that seeing apatient on a weekly basis isn't
any more or less profound thanwhat you can do with technology.
But technology for the people who want thatflavor of impact, that impact at scale.
There's really nothing else like it.
So, as I suck all the air out of thispodcast, I'll maybe pause for a bit.
No, no.
So that's great.

(07:17):
I think
typically, at this point I wouldask the guest to backtrack and
say, how did you get into medicine?
But I, I'm struggling to even comeup with the right framing given the,
like, journey you went on fromintelligent dance music, but
was there some inkling of a future
physician in you as a kid?
I know that you hadfamily connections.

(07:39):
Were you just completely dispassionateabout medicine and then you got
interested in it again after this,or just like, was there something
as part of your origin story that'srelevant to this conversation?
You know, I think that thepassion has, has been for a specific
type of very human impact.
And how I was gonna get there, Idon't think I ever knew for sure.
But once it became very clear thatthis is a path, there was a path for

(08:03):
me, that I could kind of go after.
And that there was creativity inhealth care as well, or like the
variant of creativity that I'vealways been maybe like seeking.
I think that's where, like, thatresonant frequency got unlocked and
I started to really go after it.
But there were like threepatients as a doctor, you know,
when I was in med school life.
I hated it.
It felt like torture, rote memorization.

(08:25):
Just felt, I never went to class.
I spent most of my time undergradand even much of med school,
just like skateboarding.
I had, like, diluted myself into thinkinglike, that was, it was gonna, that
was the right approach to academics.
But I remember when I went to residency,things started to really come together.
I went to Michigan and my first patientthat I saw in Michigan, is so important

(08:47):
to the moment that we're in right nowwith generative AI in care delivery.
So, University of Michigan, Ann Arbor, somany of your patients are like academics.
They're professors at the university,like highly educated people.
And I remember walking in the roomwith this newly M.D.'d swagger from
med school thinking like, I couldcome in and like tell this patient
what's up and here's the differentialand this is what we're gonna do.

(09:10):
And as soon as I walked in the room,she started to quiz me, this patient,
on whether or not she had multipleendocrine neoplasia type 2A or 2B.
That's like a, that's like anMCAT question, you know what I mean?
Like that's not a question that likeanybody has off the top of their head.
But it was like the most incredible, humbling experience.
Like, the best way to like actually be thrown into the trenches is to be humbled.

(09:32):
And thankfully, I think I pivoted prettyquickly and realized there was no way out.
I just needed to own my ignorance.
And so, on the spot Iwas like, you know what?
I don't know.
I think we should look this uptogether and maybe I can help
us navigate the literature.
So, I remember
turning on the computer in the cornerof the room, going through some of the
papers very quickly, kind of figuringthis thing out and sharing it with her.

(09:53):
And I remember, like,she appreciated that.
I think we built some type of rapport,some type of trust in that moment.
And that moment I feel is so much of
every moment anymore in care delivery,like, especially with patients
being equipped with all of thisknowledge, it takes two hands to clap.
And I think both parts of thatconversation, being able to explore

(10:14):
symptoms, medications, diagnoses,procedures, differentials, like is, is
really like the future of health care.
Yeah, I am just struck by the maturityof that response in your first year
of residency, because I know so manydoctors who have the, your Google
search isn't equivalent to my M.D. andI understand the sentiment behind,

(10:34):
behind that, given I've married into the profession, but I think that
like, it's like such a, such a unique
reaction to that type ofchallenge from a patient.
And I think most of us are gettinga sense now of like the intellectual
breadth that you've had, I guess likeall of the things that you've done had,
like, well-prepared you for that moment.
Hopefully, I think so.
Or maybe I, maybe I was caught offguard and just like somehow reflexively

(10:58):
stumbled into the right reaction.
But like humility is everything, right?
I think in health care you're gonnabe humbled on a daily, weekly basis
by some patient case, some scenario.
There's like maybe two outtathree patient stories that
have been, like, super important.
The second one was probablyas a third-year resident.
So, I'm a senior resident and my favoriteplace in the hospital is the ICU.

(11:21):
It's like the high acuity stuff, thestuff where like it's life or death
and you gotta react very quickly, andthat it's something to do with maybe
moving on to become a cardiologist.
And I was in the ICU, we saw this patient.
He was in his 40s, he wastransferred in the hospital.
He was a former pro-athlete.
And at that a pitcher, and hewas coming in with septic shock
from a rural health system.

(11:42):
And sepsis, you guys are, areprobably familiar with how the
guidelines have shifted over time.
But, like, I think one tenet of septicshock care is you wanna flood the patient
with fluids because their arteries,their blood vessels can constrict.
And if they constrict, then you're notdelivering enough blood to the rest of
the body and your organs can start to die.

(12:03):
And so there used to be thisrefrain before people realize that
that Levophed leaves them dead.
Meaning like if you give thema medication like Levophed that
constricts your blood vessels withoutputting in anything inside those blood
vessels, you're constricting nothing.
You're not improving blood pressure.
You're just cutting off
circulation.
So that's kind of what happenedin this patient before he came in.

(12:25):
He was given pressors without fluids and,over the course of weeks, we got to the
point where we were able to get him outtathe ICU when he was able to walk out.
But unfortunately, and this is areally sad story, but he needed, he
got amputations of, like, his hands.
And it's like, especially tragic,obviously given his backstory,
as a pro-athlete turned, like,physical therapist and so,

(12:46):
I remember thinking like,there's gotta be a better way.
There's gotta be a better wayto be able to get, like, cutting
edge literature, cutting-edgeinformation, not just into doctor's
brains, but into the point of care.
And certainly, there's any number ofways that we've all probably explored,
the industry has explored, trying to getthis information to the point of care.
But there's no question that, especiallywith the new types of technology, like

(13:07):
generative AI, we're gonna discovereven better ways, like even, less
friction-full ways to be able to
bend the trajectory ofoutcomes and costs.
So, Shiv, I just wannaask one follow on to that.
Yeah.
So, I think you, referencedthat after medicine residency
you went into cardiology, right?
So, you did cardiology fellowship andyou're a cardiologist, and as I understand

(13:29):
it, you're still practicing, right?
Yep.
And we're gonna get into Abridge in amoment, but I'm wondering if you could
reflect on what drew you to cardiology?
When was the sort of moment where you knewthat you wanted to pursue a fellowship
in cardiology, practice cardiology?
Can you trace it back toeither an experience in
residency or even before that?
Yeah.
Yeah.
I think it has to do withcardiology being what I believe

(13:52):
to be a really elegant mix ofprimary care and also surgery.
And for folks who have reallyshort attention spans like me, for
people who love data and certainlycardiology has seen a lot of funding.
And so there've been a lotof clinical trials that are,
there've been a lot of RCTs.
You can always find some data, so thatyou're not walking in the room completely

(14:14):
kind of naked, from that perspective.
And also, if you're someone whowants and sees that there's, like,
an incredible privilege in building arelationship, like potentially a lifelong
relationship with a patient, as youoften do in cardiology, it's just one
of those really, really magical fields.
And there's other fields like this.
Maybe gastroenterology andurology are like this as well.
But I think cardiology especiallyresonated with me for all those reasons.

(14:38):
You know, you think about the heart,and I use this metaphor with patients,
it's very much like a house.
It's like a four-bedroom house.Two rooms at the top, two rooms at
the bottom. And there's plumbing,just like plumbing in a house,
there's plumbing in a heart, andthat leads to a heart attack.
Just like a house.
There's electricity.
The electricity in the heart leads toa whole field called electrophysiology.
And that's where, like, many of the geeksin our profession end up gravitating.

(14:58):
But there's, there's also roomsthere's all the physics related
to valvular heart disease.
There's also the pump, and it'snot a water pump in the basement.
It's the blood pump, andthen you have problems there,
you get heart failure.
And so, it's just like such a rich fieldfrom that perspective, especially for
someone maybe who loves the physicsof physiology and pathophysiology
that you can geek out, but youcan diagnose the problem, you can

(15:19):
talk to the patient, build therelationship, and then you can treat it.
You can go right to the cath laband you can be the one who fixed it.
And so that end-to-end, that ownershipof the entire stack is something,
pretty rare and pretty unique.
I think you just made a reallycompelling case for going into
cardiology for some of the residentswho are listening to this. Shiv,
that was a great overview of thetraining procedure for your neural net.

(15:42):
Currently, you're ChiefExecutive Officer at Abridge.
I think we'd like totransition now and hear more.
I think we got like a littleforeshadowing, on that.
But could you tell us aboutthe origin story of Abridge?
Our machine learning colleague,I think Zack Lipton, features
heavily in this origin story.
If I have my facts right, could you tell us how this all came
together and like what you're doing now?

(16:04):
Yeah, absolutely.
So, we started the company, we startedAbridge in March of 2018, and a couple
years before that, I was in thatrole at UPMC I told you about.
And I was also overseeingsome partnerships.
So, we had a partnership that Iwas overseeing between UPMC and
Microsoft Research for B as well.
And we were going after AI andhealth care challenges together.
And that was certainly exciting,but sometime at the end of 2017, it

(16:28):
became clear to me that I really wanted
to be in the weeds and have accessto that whole stack again. But on the
technology side of things and wantedto be building with people who really
inspired me on a problem that I thoughtwould take forever to really figure out.
And that gave us like infinitesurface area, seemingly.
And I think the really, the most importantepiphany that like underpins everything

(16:48):
that we're doing at Abridge is that
we think health care is about people.
That's not gonna change.
We don't think doctors andnurses are gonna be fully
automated in the next decade.
And if they're not fully automated,then you invoke first principles
and you think what do they do?
What's one of the originalsignals in care delivery?

It's this (17:04):
it's a conversation.
Those dialogues, those conversationsdrive so many workflows in health care.
They're upstream of clericalwork, like clinical documentation
that crushes my soul every timeI see patients on the weekend.
Obviously, it's a different story withAbridge, but like, like it's a real
challenge, and those conversationsare also upstream of revenue cycle.

(17:24):
You know, in this country,we're not compensated as doctors
for the care that we deliver.
We're compensated for the carethat we documented that we deliver.
Those conversations are also upstreamof clinical trial, recruitment of care
management, of clinical decision support,ultimately experiences and outcomes.
And so, there's that surface area,you know, there's that potential.
And so, maybe like the last story that I'llshare with you that had everything to do

(17:47):
with starting Abridge, was like in Marchof 2018, and this was when I still had a
weekly clinic, a weekly cardiology clinic.
At this point, I just see patientslike one week in the month, and
I saw this patient 50 years old.
She had a 10-year history of breastcancer. And she was coming to see me
because she needed preoperative cardiacevaluation prior to starting doxorubicin,
chemotherapy that could affect her heart muscle.

(18:08):
And she was super nervous and anxious,like crawling out of her skin.
So, at the end of the encounter, I askedher why and if there was something
I did or something I said to makeher clearly feel so uncomfortable.
And she told me that for the last10 years since she was diagnosed
with breast cancer, her husband hadcome to every single visit with a
new type of doctor, but he couldn'tmake this visit for some reason.

(18:30):
And she's an English professor at theUniversity of Pittsburgh, super eloquent.
She told me that him
coming to those visits justhelped her feel more present.
And I said, why?
What does he do that's not obvious?
And she said that he's quiet.
He just, he's in the corner.
He just takes notes.
And she told me that him takingnotes meant that she felt like she
could make eye contact with me.
She could feel more presentknowing that they could go home and

(18:51):
sort of rewrite all those notes.
Google all the big words and thengo to the next clinician and feel
like the main characters of theirstory retelling it as opposed to
someone looking in from the outside.
And I think the revelation for me and forso many of us in those early days of a
bridge is that that story that she had asa patient is so similar to the story that
doctors have, that nurses have as well.

(19:12):
It's really about agency, it'sall about all of us really wanting
to build better relationships.
And have better conversationsand be better to each other.
And so wouldn't it be paradoxicallyprofound if we could somehow leverage
technology to bring those peoplecloser together, to help them make
more eye contact, to do what not onlythe doctor has to do for hours on end

(19:34):
in their pajamas after the kids arein bed and the dinner's been eaten.
But could we also do what her husbanddid, in the corner of the room?
And so threading that needlethrough the most important people
in health care patients, obviouslyfirst and foremost, is like
the ultimate ambition for everythingthat we're building at Abridge.
I mean, that was amazing.
Shiv, I love how you tied, all those different pieces together.

(19:56):
When you were thinking about thisfrom a technology perspective—
Mm-hmm.
—how did you start to formulatethe core technological approach?
You know, I think it even was notobvious to me and lots of others, like,
the doctor-patient interaction asbeing the place to insert technology.
So, I guess, like, how did, how did the,this, the technical roadmap come together?
Yeah, totally.

(20:16):
And there were venture capitalistsback then who categorically avoided
the point of care, and they would tellus like, we're avoiding that place.
We want nothing to do with it.
So, Zack would say, so Zackwas a founding advisor.
It's funny, like I remembercalling him and asking him if he
would co-found the company withme, and he was just starting
as a professor at Carnegie MellonUniversity, it wasn't exactly the right

(20:36):
time. But here we are, and he's ourChief Technology and Science officer.
So, it all worked out in the end.
But as a founding advisor, he obviouslyhas his fingerprints all over our
original approaches to this challenge.
And he would scoff at the ideaof attention is all you need.
Potentially changing the trajectory of ourearly research, because certainly I think
to folks like you, to the researchers outthere, probably like everyone was already

(21:00):
experimenting with transformers and tryingto figure out their place and their place
in workflows like this in health care.
But that was certainly themoment where we started.
We started with pre-trained modelslike Burr, bioBert, long-form Pegasus.
Like we experimented with them all.
In August ’21, Zack and his lab publisheda paper and presented a paper at ACL

(21:22):
that really kind of like started tomake clear that we could do this.
And this is pre-LLM.
And in that paper, he described this twostep, two stage modular summarization
pipeline, basically to create doctornotes, also known as soap notes.
And the first step waslike utterance extraction.
And again, we were using a BERT-basedmodel, like it was fine-tuned, but that

(21:44):
model would classify every utterancefrom a dialogue, from a transcript
for quote unquote note worthiness.
And then there was an abstractivesummarization piece or step
where we would use a fine-tunedlike T-five transformer model
to basically write the note inthe style of a doctor's note.
And we were leveraging this huge corpusof data that we had aggregated and paid

(22:06):
for to annotate over many, many years.
And that's really, so much of ourearly days as a company was about.
Kind of like doing two things at once,walking and chewing gum, if you will.
The walking piece was aroundthis long game around R&D
that we were embarking upon.
But then the other aspectwas we didn't hold our breath
either to, like, learn from users.

(22:27):
So, we put a consumer appout, believe it or not.
So, like recognizing that it takes twoto create that dialogue and that the
patient story and being able to servepatients over time was gonna be such
a core kind of aspect of our mission.
We put a free consumer app out in
July 2019.
And so, while that was going, we were alsoasking those users for permission to their

(22:47):
data so that we could then add, annotateit, aggregate it, create these new
notes that were annotated in exactly theright ways with all the right timestamps
to be able to go after this problem,
like, with those types of models.
So should that, that's actually thequestion that I wanted to ask, which
is, so you guys started in 2018, right?
And this is now hard to believe,but this is like maybe three, four

(23:10):
years before ChatGPT large languagemodels and the current moment that
we're in technologically, right?
Where now I'd say we have,
we went past the ChatGPT moment, andnow we have this rich ecosystem of
competitive LLMs at the foundationlayer, both proprietary and open source.
And so, what I'm really curious aboutis, how you have navigated that sort

(23:34):
of technological shift from 2018BERT-based models, very different
I imagine ways that you wereapproaching or even thinking about
problems then versus the sortof LLM-era that we're in now.
How have you adapted the technology,what you're doing, the models, how you
even approach R&D or even maybe even howyou think about R&D for the company?

(23:58):
Yeah, it's a great question.
It was 2022 when we started to pullLLMs into the stack, and where we
obviously saw all, like, these stepwiseimprovements in terms of the output.
And I'd say the way we wereinitially integrating LLMs in
was fairly basic compared towhat that stack looks like today.
Today,

(24:18):
we have this whole reasoning engine,and it's any number of different
models, maybe like 20, 25 modelsthat are all orchestrated together
in order to create that clinicallyuseful, but also billable node and all.
And where there's also models thatare looking for hallucinations, like
guardrail models or models that are doinginformation extractions so that we can
template orders inside the medical record.
And so,

(24:39):
the level of complexity, youknow, now compared to 2022,
it's like off the charts. But whatour prepared mind and all that
potential energy that we had storedup from all those experiences
framing these challenges, albeitleveraging tools like BERT and
T-five. What it meant is that in 2023,
as we were inserting and starting toleverage LLMs in our stack, we just,

(25:03):
all that potential energy turnedkinetic immediately. And it's because
of multiple things happening at thesame time, like multiple stars aligning.
The most important star for any companyis the market. You know, you can have
the best product, the best technology.
If you don't have like a realmarket problem, you're not gonna
really create that much impact.
But in this case, the market needed this.
It still needs this.

(25:24):
Two outta five doctors don't want to bedoctors in the next two to three years.
27% of nurses per JAMA don't wantto be nurses in the next 12 months.
There was one surveythat suggested that like
60% of medical students don'twanna be full-time clinicians,
you know, when they graduate.
So, we have this public health emergencywhere patients are having to drive
sometimes five, six hours from ruralsettings because their hospitals have

(25:46):
shut down, and that's the only way theycan see a rheumatologist in the inner
city hospital who can prescribe thebiologic that can save their lives.
So, we have to do somethingto address this challenge.
And so I think what that led to in2023 was C-suite executives across the
country understanding, recognizing thatwe need tools for the first time ever,
maybe. We need automation ina way that we didn't before.

(26:07):
We need to augment clinicianssomehow by hook or by crook.
So what I didn't know was thatall those other years, those years
leading up to 2023, I was pre-selling.
So, when we were doing these demos.
And we were leveraging that stack thatI was telling you about, that Zack
published on that, that his team built.
We were really pre-selling because thatproduct, by the way, was very compelling.

(26:27):
It wasn't quite as magical as wherewe are now, obviously, but it could do
a job for some clinicians out there.
And so, all those C-suite executives wehad demoed for who didn't have time
before, for whateverreason, they called us back.
And one of the big learnings,like, I think anyone building in
health care AI or health care techin general needs to understand is,
like, health care is not homogenous.

(26:49):
And on one end of the market there arethe large integrated delivery networks,
the academic medical centers, the
payer providers, the complex systems.That's where 70% of doctors are.
On the other end of the marketthough, there's the direct primary
care doctor down the street.
There's the independent PCP, there'speople taking cash payment out of
pocket, off the insurance grid,so to serve the individual doctor.

(27:12):
Or the small provider group is atotally different undertaking than
serving the large complex system.
And so that's also probably whyin any given space in health care,
like any given use case, you mightsee a ton of startups competing.
But then there's probably only twoor three who are trying to actually,
or who have this stuff to actually
try to serve those large systems whereyou have to come correct off the bat.

(27:33):
You have to serve all the, in our case,all the specialties, all the different
settings, outpatient, inpatient,urgent care ERs, and in our case,
all those different spoken languages.We're in Boston, for example, today.
And there's, where you guys areand there's probably gonna be
tens of thousands of conversationsspoken on Abridge in Haitian
Creole, and Brazilian Portuguese.
So yeah, it's a differentkind of complexity.

(27:54):
So, you know, you mentioned some ofthe technical challenges that you
address by having this mixture ofmodels and this complex system that's
underneath the actual product itself.
And one of the ones that caught myattention was hallucinations, right?
So, this is something that we've beentalking about with LLMs and with ChatGPT
that I think has nowentered mainstream, right?

(28:16):
With the general LLMsfrom the tech companies,
we all know that they hallucinateand I think they still do, right?
I think this is not a solved problemat that sort of base layer, but
what's interesting is now there arecompanies that are serving kind of
medical needs like yours, like a fewothers that are trying to address
this problem, right?

(28:36):
This problem with the base layer, withthe LLMs that are involved in your stack.
And I'm curious, you know what, whateveryou can tell us about how you solve
that and how you sort of validate,
'cause it's actually, it'sa very hard problem, right?
To even know how much your system ishallucinating to have well adjudicated
labels of what a hallucination is.
To take expensive sort of human timeto verify whatever are the automated

(29:00):
parts of the evaluation itself.
Like how do you really detectand reduce those hallucinations?
How do you approach that problem?
Maybe just to dovetail into that,one thing I get asked a lot is how
do you know these products are safe?
And I think that that's like adistillation of what Raj is asking.
That's, that's a shorter andbetter question than what I just
asked, so let's go with that one.
Yeah.

(29:21):
And safety is a really interesting topicbecause in many ways you could argue
that health care, like, the health careindustry has adopted AI faster
than any other industry out there.
Over these last few years, likehealth care has taken up AI and scaled AI.
Unlike any other industry, it's wild,it's historic, it's never happened before.

(29:42):
We're live in well overa hundred health systems.
We're at scale.
We're probably touchingmillions of clinicians and
millions of patients every week.
And that happened relativelyquickly over a few years, and
that's never happened before.
When I was a corporate VC, if wehad invested in a startup that was
provider facing and the founder said,you know, that they, they, they closed

(30:05):
two pilots a quarter, it'd bechampagne bottles all around the room.
Like, going to health systems foroftentimes for the right reasons means
you, you need to be prepared to eat glass.
It's gonna take a long time to inflect.
It's gonna take a long time tobuild trust, but I think because
of all these, like, sort of,
challenges in health care because oflike burnout and burden and all the

(30:27):
other supply-demand mismatches.
That certainly created a moment.
But what happened, what we sawhappen was that the health care
industry sort of intuited a kind oflike risk profile around a company.
So, what's really taken off over these last few years are
companies focused on use cases thatare high frequency and low stakes.

(30:48):
So,
documentation when the clinician isin the loop can to trust and verify.
And when they also have tools, like, wehave a feature called linked evidence
where you can highlight a word, sentencefragment, paragraph, and we show you where
the evidence came from, all the way downto the actual associated snippet of audio.
You can trust and verify at that level.
You can, it's all auditable.
And so with that human in theloop, that clinician in the loop,

(31:11):
we certainly de-risk, I think,many of the issues associated
with AI at the point of care.
But then you think about the other end of this matrix, high risk.
So, high stakes and high frequency.
I think clinical decision support,for example, in the ICU, I think about
all the sepsis work and like whatit takes to actually deploy sepsis
predictors to the point of care.
Those companies have to verify, theyhave to validate, they have to publish.

(31:33):
It's a different type of gauntlet.
Yeah, I think that's very well said.
And I think what is fascinatinghere is that we have evidence, about
the accuracy, the diagnosis rate.
We're almost sick of these studies now,
all of us, right around—shouldbe careful with what I say.
But we're, you know, all of us are

(31:53):
very used to these studiesshowing multiple choice
question dominance of LLMs.
But I think what you're saying is that the use cases are very different
from one another in terms of therisks attached to different errors.
And I think you're right.
Also, clinical decision supportfeels different than documentation.
Although I think documentation, myguess is you would say it's also very

(32:14):
important not to hallucinate there.
Mm-hmm.
As well.
And it's important that it reflects whatactually happened and that it reflects
the patient's story going back to the wayyou so eloquently put it at the beginning.
And so, I think this is still a generalchallenge that the field is facing not
only for the sort of the foundation,the base layer, but I think for the
companies that are providing medicalinformation, whether it's through,

(32:38):
documentation, the servingmedicine, right through documentation,
like your company. Or I think someof the others that are trying to live in
the space of providing useful informationto physicians at the point of care?
I think of something likeopen evidence, right?
This is a hard problem thatthey're trying to solve around
hallucinations, around the accuracyand the fidelity of the information
that's provided and the references.

(32:59):
And I think it is an open both academicand technical problem and very interesting
to sort of hear how you think about it.
It's actually pretty relatedto that line of questions.
You mentioned something about trust,and I think this is a big part
of what you have done very well.
You have doctors trusting yourproduct and I'm wondering if you can

(33:20):
tell us, how did you go about that?
How did you actually getdoctors to trust Abridge?
Yeah, trust ends up beingthe only currency that matters
in health care. Clinicians orexecutives inside of a health system
only want to know so much aboutyour technology and your stack
and all your guardrail models.
They wanna understand thatit works, that it's reliable.

(33:42):
They wanna understand if you aretransparent and if your technology
is somewhat transparent and audible.
They wanna understand ifyou're credible people as well.
And so, I think for us, transparency,reliability, credibility
are the most important.
Those are the dimensions of like trustthat we focus on, and we just try to
unpack what that could mean at everysingle layer of our company.

(34:04):
Whether that's on the go-to-marketand sales side, whether
that's on the technology side.
I mentioned that feature linked evidence.
We published white papers likewe did one some months ago
where we sort of broke down
a lot of our kinds of evals, andour benchmarks, and how we hold
ourselves accountable, and what ourword error rates look like today.
And, like, our models are tryingto continually improve we try to

(34:26):
teach our clinicians and our healthsystems about preference tuning and
DPO and try to tell 'em about likereinforcement learning and our LHF
and what that means when we go live.
And I think that transparency,like, they appreciate, too. But
I think ultimately for us,
the challenge in health care, likethe exciting part of that end of the
spectrum, like the large end of thespectrum, is that we have to thread
these needles. And number one, thishas to work for the end user, and it's

(34:50):
not just any end user, it's like allthe different specialties, et cetera.
Number two, it needs to work forexecutives, and one is the CMIO.
The CMIO wants to know roadmap.
Where are you going?
Are you credible people?
Can you get there?
Do you have science at thecenter of your company?
Who are those people?
Do they have the stuff, do youhave proprietary data sets?
Like they might ask those questions tounderstand that like these slides that

(35:10):
you put in front of them, that you'regonna be able to execute against them.
There's the CIO.
And the CIO has any number of concerns.
But some of those concerns relateto can you integrate, can you be a
credible part of a very complicatedstack of investments that they've made?
Are you gonna be aroundfor the decades to come?
Are you funded as such? How muchare you investing into R&D?

(35:32):
How do you think about enterprise?
Do you have all the certifications?
Can you check off allthe compliance boxes?
But then there's the CFO.
And that's like the third mostimportant, or maybe they're all the same.
But like that, that's the thirdstakeholder that we encountered
over these last few years.
And now they're a regularpart of any partnership.
And the CFO wants to know the bottom line.
They wanna understand the metricsand any more of the metrics.

(35:54):
Are not just around burden or burnout.
They're around, revenue cycle.
Because again, like thesenotes are bills essentially.
And so, like, how do we quantify theimpact that we're creating and helping
clinicians capture and get credit forthe complete care that they delivered?
Fantastic Shiv.
Actually, maybe one finalquestion then, I think we're
gonna jump to the lightning round.

(36:15):
Could you give us like a quick summaryof what scale you're operating at now,
and to whatever extent you can, a previewof the roadmap for what's next, where
you're looking to expand within the UnitedStates, outside of the United States.
What's on the horizon for you?
Yeah, so far I'm happy to share thatwe're live in over a hundred health
systems across the country, andthey tend to be large academics and

(36:37):
integrated delivery networks, andthey tend to also be scale deployments
across specialties and settings.
That means we're touchingmillions of lives a week.
So, we're impacting millions of patientsand doctors every week, which is obviously
incredibly fulfilling and exciting.
The roadmap is equally exciting.
So, we've raised a lot of capital overthe years, and 80% of the capital that

(37:00):
we've raised is really all about researchand R&D. It's all about R&D, like 80%
of it needs to be invested into R&D.
We believe that will translateinto just, like, better product.
That's what it means to play a long game.
Like at the end of the day,it's just about creating impact.
So, what that impact looks likeis just changing so quickly.

(37:20):
So as an example, about a monthago we announced what we call
a contextual reasoning engine.
That reasoning engine is pulling indisparate data from multiple sources.
Including the medical record,but also including payer systems,
including rev cycle systems, includingtextbooks, including coding best
practices, including potential rulesthat are a health system specific.

(37:45):
I remember I used to be on a clinicaldocumentation improvement team at UPMC and
we would go from department to department.
We do these lunch and learns, pizzaand PowerPoints, try to teach doctors
how to write notes that are billable.
How to reach PCPs.
What risk adjustment is. What an HCC is.
How an HCC maps to an ICD.
How to think about me criteria.
Make sure to include what you discuss.

(38:06):
Monitoring, evaluating,assessing, or treating,
'cause that's gonna make a hugedifference on how much you get
back, how much credit you get, andhow you're compensated by Medicare.
Every single doctor wouldhave a thousand yard stare.
Nobody, no doctor wants to be told howto be a biller or a coder or an auditor.
No one went to medical school or nursingschool to memorize any of that stuff.

(38:26):
They just wanna go backto see their patients.
And so what happens when AI, when likethis age agentic system in the background
that's not even in your face, is actuallytaking care of all that work that
nobody wanted to do in the first place.
That's really what thatreasoning engine's all about.
And I think that's where we'll becontinually investing, but also,

(38:47):
like scaling across more settings.
So, in the coming weeks we'llbe scaling across inpatient.
We've mostly been in theoutpatient space, urgent cares, ERs.
Now we'll start to get intothe inpatient world as well.
Wow.
Fantastic.
Andy, are we ready forthe lightning round?
Shiv, are you ready forthe lightning round?
Yeah, I think so.

(39:07):
Awesome.
So, let's hop into it.
We rewrote some of these onthe side based on your intro.
Uh, 'cause I think your background, it'll be fun to get your take on them.
So, do you think things createdby AI can be considered art?
Yeah, absolutely.
Could you give us a little, give us alittle, I mean, so, so brevity is the soul

(39:28):
of wit, and you nailed it by that metric.
Uh, but give us a little bit more there.
I, I, I think art is as much about whomade it as the impact that it can create.
And how it can affect you as a human.
And so, if something generatedby AI can impact me in a very
human way, then you know, I wouldconsider that art personally.

(39:50):
Do you think that artis, I'm gonna get way?
Oh geez.
I don't know that I'm likethe right guy for all these.
No, no, no.
That's right.
There's do you, most people are reactingthat there's a recognition of a shared
experience or something in art sometimes.
So, so maybe, I don't knowif, if AI, like, do you think.
Is there something about an aggregateshared experience that a generative
model is conveying that's stillrecognizable as a shared experience?

(40:12):
Or some people would saythat it's just vacuous.
Again, way outta my lane here.
Yeah, I don't know ifthere's anything to that.
I think there is something to that.
There, there are like some philosophersout there that talk about an aura around
art that's been created by a human.
And I don't know what that means.
What I do know though is that somany of my favorite artists are like

(40:34):
increasingly leveraging technology orthey've always leveraged technology.
We had a company conference lastyear called The Conversation where
we were explicitly just trying tohave the weirdest, most inspiring
kind of firesides we could imagine.
Like just put people togetherthat maybe shouldn't be talking.
And I got to have afireside with Rick Rubin.

(40:56):
I don't know if you know who RickRubin is, and it was awesome.
The vibe master legendary.
Legendary music producer, right?
There we go.
Here we go.
Yeah.
We have a company value.
You have to taste goodthings, to have good taste.
And I think like now more thanever, like taste is a really big
part of how you build defensibilityand moat in, in the AI world.
It's like sensibilities mean so much.

(41:16):
My twin 8-year-old boys are likevibe coding right now in Cursor.
They just got through a hundreddays of Python and Replit and
they don't have good taste.
It's very clear like they're gonna haveto create, figure that out over time.
But Rick made this point aroundthe wah-wah pedal, interestingly
enough, and how that was like reallyradical technology at one point and
very few people wanted to adopt it.

(41:38):
But then this sort of like
guitarist who was not well knownat the time figured out that he
could express himself in a, likea totally differentiated way.
And then he changed his name to JimiHendrix and the rest is history.
And I think that more people will figureout that like, AI is a superpower that
we can all leverage in our daily lives.
I totally agree, and I also totallyagree about the importance or

(42:01):
the growing importance of taste.
I think it's always beenthat very, very key factor.
But I think, in this era whereyou can produce so much, so easily.
Totally. Taste, and taste andselection is so much more important.
Alright.
I have no idea what you're gonna sayto this next lightning round question.
I've been trying to simulate and tryingto guess, but I truly have no idea.
I feel like I just built it up too much.

(42:22):
It's fine.
But what is your favorite novel?
Oh man.
Um, that's, that's a, that's a hard one.
I guess like different, differentnovels for different reasons.
I don't, there's not one novel.
The book that like maybe had themost profound impact on me growing
up was Autobiography of a Yogi.
I don't know if you've ever read thatbook, but for anyone who has a tendency

(42:45):
towards magical thinking in any way, shape,or form, it's a pretty profound book.
I feel slightly contrived mentioningthat book because apparently it was
one of Steve Jobs's favorite books.
And I don't mean to like bring up abook that like tech companies or Silicon
Valley folks, might revere forwhatever reason. But certainly growing up.
I was born in Pittsburgh, but Ilived in India from age nine to
age 15, and that was a book thatI was gifted when I got there.

(43:08):
And I remember it was, like, just wild.
Like I just could not believe whatI was reading about these yogis
and the things that they could do.
And it makes you wanna believe inthis other plane, like, of existence
and magic, frankly.
In college, I'd say like themost important book to me was
Franny and Zooey by Salinger.
I just remember reading about one ofthe character's steadfast dedication

(43:31):
to trying to find meaning and she waslike repeating this mantra like over
and over and over, trying to findmeaning and finding there to be sort of
some, some profundity in that as well.
There's like something meditativeabout getting obsessed with
something that's worth it and shehad something that was worth it.
And you know, maybe atthis point in my life,
I'm working like I did as an intern,and this will be the rest of my life.

(43:54):
But this is how the last likeeight, nine years have been
certainly like 80 hours a week.
And that's how so many ofus are working at Abridge.
And it's worth it.
And I think we're doing it because welove it, like, we we're obsessed with it.
And that's what that bookmeans to me a little bit, too.
Amazing.
Awesome.
Thanks, Shiv.
So, speaking of different planesof existence in an alternative

(44:17):
universe, if you weren't a doctor,an entrepreneur, what would the
alternative universe the alternativeShiv be doing for his professional life?
So, in our Slack last night, wewere there was like this thread,
where we were talking about,
I think I had brought thisup, but I had brought out how
Shaquille O'Neal owns 150 Five Guys,

(44:39):
17 Auntie Annes, 150 car washes, 40 24-hour fitness centers, several
Las Vegas nightclubs, and a movie theater.
And my argument was thathe is way better prepared for
like AGI than like any of us.
Like he's just like, he's totally there.
He is gonna crush it.
But we were trying to like, I wastrying to like debate or bring—. Sorry,

(45:01):
why does owning a lot of franchisesmean he's prepared for AGI?
Uh, uh, you think about physicalAI and I'm sure Jensen would say
physical AI is like coming prettysoon, but like, I wanna believe
that it'll take a little bit longer.
And a lot of those, like physicalexperiences, a lot of those like types
of jobs and experiences that like humansstill want are gonna be around for a beat.
But like, maybe this somehowdovetails with the taste thing,

(45:24):
but one craft that I'm excitedabout is Japanese indigo farming.
And it's like this monastic almostlifestyle that these folks have.
And it apparently can take hundredsof hours to make one garment.
But they're YouTube videos that detailevery single step of the process.
And like a few of us were likeunpacking some of those details in

(45:45):
a Slack channel last night.
But if I could go have thissort of monastic, almost
Walt Whitman sort of like lifestyle off the beaten path, like, you know,
focused on indigo farming, thatsounds pretty awesome off the AGI path.
Nice.
Alright, Shiv, our next question is,will AI and medicine be driven more

(46:05):
by computer scientists or clinicians?
It is neither, I thinkit's gonna take all of us.
I think that's where the magic's gonna be.
If there's one thing that I, I reallywill die on the hill of is like domain
transfer and, you know, having yourfoot in multiple spots and like learning
from one domain and like pullingthose learnings into another.

(46:27):
And putting weird combinations together.
Like that sort of alchemy is wherelike really creative stuff happens.
And so like, we need that.
This has been such aconsistent theme of what
guests have said on the podcast too.
And it's like the existence of both of those skills within the
same mind is very different thansort of two experts coming together.
A hundred percent.
Who can't actually, who can't actually,there's, it's a latency, right?

(46:48):
Yeah.
They can't actually, they eithercan't talk to each other or they
can't see the connections becausethey're, there's no sort of shared
vocabulary and ability to likerapidly iterate on what a good idea
is from one field to the other.
Yeah.
So, it's great to me
'cause it's been likesuch a consistent theme.
Yeah.
Of the, of the, yeah.
Zack has—Zack, our CTO,
he's a professor Carnegie Mellon,but he, he's like obviously fully

(47:08):
focused on Abridge right now.
But he has a team that he calls themutants and the mutants are medical
professionals who are actually like
very serious computer scientistsas well, or programmers.
And so those people just have like thesemultidisciplinary meetings in their
own mind, you know, like they can skip so many different steps and
oftentimes they also have like taste, too.
And so, you're getting these intangibles.

(47:29):
Totally agree.
Totally agree.
Cool.
Awesome.
Last question.
If you could have dinner with oneperson, dead or alive, who would it be?
So, my reflexive take is Yohji Yamamoto.
You know that guy, like, like Japanesedesigner, but he's still alive.
And somehow, I don't know how, but Isomehow I wanna meet him while he is
still around. But just like a pretty,so he just, to give you a sense like,

(47:53):
he's played one note his entire careeras a fashion designer, but it's been
the right note, and he's stuck to it.
Like some ideas you need to holdvery, very, very tightly to,
you know, and like for a companythat ends up being your mission.
And if you can be blown with thewind, then who are you anyway?
And like Yohji from a very young age,had this very ascetic aesthetic and

(48:15):
he's held so strongly to it thathe's like, you know, produced entire,
uh, entire fields of people who arejust like following in his footsteps.
This just continuing with our themeof taste and the aesthetic and the
sense of—. Yeah, I'm like the worst person.
All important, all important.
Talking about all this.
It's good.
It's good.
No, it's fantastic.

(48:35):
I think, I think it's both interesting,but I really don't, I also think it's
not accidental in your success as well.
So actually, this is the, this isjust, so, first of all, you did
great with the lightning round.
We're done with that.
Awesome.
And we just have two, sort of final questions for you, Shiv.
And there's sort of big pictureconcluding questions parting
thoughts that we'd love your take on.

(48:56):
And I think we, the first one,we've actually talked about this,
I think a fair amount, but maybeyou can try to distill this.
There's a lot of medicalstudents, residents,
interns, fellows, young physicians,physicians in-training who
listen to the podcast.
And I think a lot of them are gonnalove this episode because they're gonna
love, they're gonna imagine themselvesin your shoes and how they could

(49:18):
sort of get there, how they could beleading an AI company as a physician.
How they can bring theirclinical perspective to AI
and how they can get involved.
And so maybe you can justdistill down for the physician
audience, how can physicians
like yourself, becomeleaders of AI companies.
I think that what clinicians sometimesmight lose track of is, now more

(49:40):
than ever before, you can just dothings, you can just learn things.
You can just go deep.
You can just go build. Like the distancebetween idea and execution right now,
especially like on a prototype for exampleis smaller, is narrower than ever before.
And so, the ability to validatesomething that could be useful in the
world, is easier than ever before.

(50:01):
Now scaling and all the other thingsthat like need to come later is
a different matter, but like youcan get your hands dirty in a way.
Like the barrier to entry on beingable to like just go build and
pursue what you're really passionateand obsessed about is not there.
And so, people should recognizethat and go after it.
I think even for clinicians likeolder, like me I think what we

(50:21):
sometimes forget is that so much ofour training has equipped us for the
tech world, for the business world.
The example I use a lot is likewhen I'm in ICU and you're seeing
a patient who's dying. With respect,obviously I'm like abstracting, lessons.
But a startup is like a dying patient.
You are forced to figure out to react.

(50:42):
Very, very quickly.
You build a prototype, you deployit in the ICU, you start pressors.
You start up inotropes, you measureare things improving or not,
and then that helps you decidewhether or not you persevere.
Or you pivot and you just go through thesebuild, measure, learn cycles as a doctor
in those acute settings, the same way yougo through build, measure, learn cycles
as a startup, especially in those earlystages and sort of getting outta the ICU

(51:04):
is like getting to product market fit.
Getting out of the hospital is maybe likefree cash flow or profitability, but like
there's a lot of like lessons that you cantake from your training as a clinician.
All the people you managed, thoseresidents, those trainees, those
pharmacists on your team, like how youcoordinated care, how you insured people,
were also like growing in their roles.
Like all of that, I think I'vebeen able to like leverage

(51:26):
in some way, shape, or form.
But the most important thing isjust surround yourself by really,
really amazing people and that'swhere I've been the luckiest, I think
the mission has been pure enough.
That we've been able to attract peoplelike Zack and Julia, our COO and Brian,
our Chief Commercial, and, Saga our CFO.
Like the list just goes on and on and on.
And those are the people,honestly, that I'm learning

(51:46):
way more from, than vice versa.
Cool.
Last question, Shiv.
So you often hear folks like EricTopol say that AI will restore
the doctor-patient compact.
And that presupposes a sort of platonicideal of what the relationship between
patients and doctors was at one time.
But I, I guess I'd like to rephraseit and say like, how will AI change

(52:10):
the doctor-patient relationship?
You know, I think you, you saidyou get more eye contact now.
Mm-hmm.
But what do you thinkit looks like going forward?
I think some of us doctors have nostalgiafor something we've never really known.
You know, we have this like idea, thisromantic picture of a mid-century doctor
who visited you at home, put your, puttheir hand on your back when you were
suffering and knew your grandparents andalso your children, and helped you create

(52:32):
the Google Maps to whatever your outcome,your chosen outcome, your personalized
outcome was, or you wanted it to be.
And I think that we're seeinga path to that sort of romantic
story, but that's not terminal.
Like, that's not the end.
We're clearly getting to a world whereit's more like that first patient I saw
at Michigan where roles I think will getincreasingly blurred lines, and we're

(52:55):
both gonna be there for each other.
But maybe one thing I can kind of leaveyou with is just like, why we should revel
in the moment that we're in right now.
Inside of our company, likeHIPAA compliant, you know,
sort of communication channel.
We have a channel called Love Stories,and so every day we get positive feedback
from users, and I think most of theseusers are sort of like realizing like

(53:16):
maybe that that nostalgia that theyhave for something that they own, maybe
it's like it's coming true right now.
So, this comes from a ruralhealth PCP at Tanner Health.
And she writes to us,this is some time ago.
This is one of my favorites.
I was sitting at dinner last weekand my son asked me, mommy, why
aren't you working right now?
I literally took my phone out andexplained to him that Abridge is a
new tool that lets mommy come homeearly and eat dinner with her family.

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I started to tear up and lookedover at my husband, who then said.
Mommy's gonna be able to eatdinner with us every night now.
That's the moment, right now that,and that's the moment that we've gotta
scale as fast as we possibly can.
Everybody deserves that and the patientexperience is improving as well.
And then I think like, we'll allgradually and probably pretty
quickly actually relatively speaking,like move into that new world of

(54:00):
blurred lines and where like knowledge isjust basically completely democratized.
And it's more about, maybe toour other conversation, it's more
about taste and sensibilities andlike empathy and like humanity.
But we have a lot of work to do likein the right now with technology.
Cool.
I think that's an excellentplace to end, Shiv. So, thanks
for joining us today on AI Grand Rounds.

(54:21):
Thanks so much, Shiv.
That was great.
Thank you.
It's been a privilege,this copyrighted podcast.
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(54:42):
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