Hello, everyone. This is Jacob Emerson with the
Becker's Health IT podcast.
Thrilled today to be joined by doctor Nigam
Shah, who is the chief data scientist at
Stanford Health Care. Doctor Shah, thank you so
much for taking the time to be with
me on the podcast today.
Oh, great to
be here. Yeah. And before we dive into
everything we wanna talk with you about, Nigam,
can you tell us a little bit more
about yourself, your background in health care, and
what it is that you do today at
Stanford?
Sure. Sure. So I often joke I've never
had a real job. I got my PhD
from Penn State, came to Stanford as a
postdoc in 02/2005,
and never left.
Been on the faculty since 02/2011,
tenured in 2015.
And then I was doing the usual, you
know, writing papers, real world evidence, machine learning
on EHRs and so on. And, got sucked
into working on things during the pandemic in
the health system
and found it very rewarding to do things
in an operational environment. So in 2022,
we set up this new role of chief
data scientist for the health care system where
the job basically is to translate things
into practice such that patient care directly benefits.
Fantastic. So, Nigam, we're here to talk with
you today about an announcement that Stanford HealthCare
made earlier this year saying that the system
was going to be launching an Ambient AI
pilot with Atropos Health to integrate real world
evidence within the electronic health record
via Ambient AI listening,
from from Microsoft decks. So talk to us
a little bit about this.
How is this gonna be working, this pilot,
and and how is this gonna help your
physicians
access real world evidence to make better decisions
for your patients?
Mhmm.
So real world evidence has been part of
my work
almost since 2011,
2012.
And as you probably know, I mean, most
of the times, real world evidence is an
observational study that takes anywhere from six to
nine months or maybe one year. It gets
published in a journal like JAMA or New
England Journal or Lancet or what have you.
And maybe a physician reads it or doesn't
read it. And the chance that it affects
patient care is
very low.
There's some very shocking numbers out there saying
where physicians tracked the number of decisions they
make, they go back and ask how often
was some evidence available
particularly relevant to that case at hand. And
often the answer is, you know, under five
percent, under three percent, things like that.
So that's what I've been researching.
And we did a project here called the
green button project that
took that nine month time frame down and
got it to under two days. So at
the bedside, a physician can make a request
and a team produces a written report,
in two days.
Once that was done,
we spun out Atropos Health,
which took those forty eight hours, turned around,
and got it down to under three to
four hours.
So at the bedside, you have a question,
you ask something, what happened to similar patients,
and you get a written report before the
end of the day.
So then Ambient AI comes around,
and number of physicians
are super excited about how it's now saving
them time and freeing up their sort of
cognitive
bandwidth to think more about the patient.
On the other hand,
Atropos adoption is going fine. About a thousand
or 1,200 consultations
are requested and reports being produced within within
a day.
Now separately,
not at Stanford,
Atropos had been developing this solution, which they
call CHAT RWD,
which essentially automates this
procedure of producing
observational studies on demand,
takes the human mostly out of the loop,
and can get an answer,
for a lot of cases, in under five
minutes.
So now you look at these two things
together and you ask, alright.
We have a patient visit that's fifteen minutes
now getting transcribed, and in about a minute
or two, you get the full transcript.
We have this separate ability
that Stanford has already purchased
where the physician, instead of a day, can
get an answer in five minutes.
What would happen if we put the two
together?
So that led to the current project you're
talking about.
Wow. That's that's some incredible numbers you decided
there in terms of,
five minutes and getting an answer back. That's
incredible.
I I wonder, have you heard from clinicians
across the Stanford Enterprise in terms of how
this is being received, what their thoughts are?
Is this, being received enthusiastically?
So lots of enthusiasm
to pilot it and be, you know, the
first to get access to it, 100%.
The Atropos service, the one that produces this
report
has been live on our campus since almost
2022
actually or 2021.
Okay.
And that typically is used in the inpatient
setting because there you can tolerate a one
day or a four hour turnaround. Right?
Ambient scribes are used in the outpatient setting.
And so this will be the first time
that real world evidence will be offered in
the outpatient setting on demand
at the point of care. So everybody's super
excited about it.
Wow. No. It's incredible. And is it my
understanding that this is going to be continued
to be rolled out this year? Is that
the general timeline for this pilot?
Absa absolutely. Definitely before the 2025
ends. You know, most likely, the first actual
user will get it in the next sixty
days or ninety days. Wow. And and this
is all your physicians across Stanford?
No. We'll be actually piloting this with what
we call our, community practice providers or UMP,
university medical partners, or sometimes called Stanford medical
partners. Sure. So these are physicians who are,
like, not at the university. They're in the
community practices all over the Bay Area. And
they're the ones who are super excited that
finally,
technology is at a at a stage at
ease of use that they can incorporate into
their care. Wow. That's amazing. And and I
know we did briefly just touch on this,
but if from the patient's perspective,
how would how would this,
on the ground? What does this look like
to me? I come into to the office
and I receive those answers within five minutes,
or can you can you detail that for
us a little bit? Mhmm. So imagine before
all of this technology, you go to a
physician.
You're speaking. They're looking at a computer screen,
typing away furiously so as to not to
miss anything.
They maybe glance at you once or twice.
And then, you know, once their note is
done, they look at you, tell you three
things you should be doing, and off you
go. Right?
Ambient scribes come around, and now the doctor's
actually looking at you.
And
there's this thing in the ether that's transcribing,
and by the time the the, the conversation
ends in about a minute, a note shows
up, which they can turn the screen around
and and show it to the patient directly.
In fact, our CMIO, doctor Topher Sharp, has
done even video interviews with reporters showing them,
you know, how powerful this is
in terms of
the physician being able to pay attention to
you. Right? So in normal encounter, everything is
fine. This automation describing
sort of does the job. But then if
there's a question,
what does a physician do today?
They will go to UpToDate or some other
literature summary.
Or
if they had a question that can't be
answered by literature, they will trigger this real
world evidence report from from Atropos.
But that's a manual step, both of them.
So now you can imagine a situation where
we fully have the transcript and it's electronic.
Can I distill a question out of it,
send it to literature evidence,
send it to Atropos for data driven evidence,
produce both of them back if something exists
and is relevant, and put that as two
links in the note that is being shown
to the physician? And then they can choose
to say, alright. Yeah. This looks good enough.
I should click on it and read it
and talk to the patient about it.
Understood. No. This is incredible. And I it
it really reflects what we're hearing from a
lot of other health systems around the country,
piloting and introducing this type of technology as
well.
So
in that vein, Nigam, what what would you
say have been some of the key challenges
that systems,
maybe Stanford alone, but from other systems as
well that you're hearing,
challenges they're facing when trying to integrate ambient
AI tech into their existing workflows?
Yeah. So there's sort of two broad buckets
of challenges.
The first one are sort of the the
the seemingly daunting ones where or the easy
ones, I would call them technical challenges. You
know, what exact API are you gonna call?
What's your latency?
How fast can you get the answer? How
will you maintain security, privacy, compliance? You know,
all of that,
excuse the word, junk that goes in with
health IT in general in in in doing
things with duct tape and bubble gum and
string. Right?
I'm sure.
And then the second bucket are the challenges
we haven't yet thought of, which is how
are we gonna evaluate these things? Because, you
know, given a Google query, for example, we
always get an answer. Given an up to
date search, you get an answer.
Given an RW real world data search, you'll
get an answer. And there's some safeguards built
in that if the cord's too small or
the phenotypes are not clearly defined, the system
will refrain from answering.
But when you put all this together,
how do you evaluate?
How do you know it's adding value?
Those are two different kinds of evaluations. Those
are the things I think in the next
year, everybody is gonna be, thinking about. Yeah.
No. Absolutely. It's a great point. And I
that leads me to my next question for
you is, how do you think that Ambient
AI tech within the hospital, how's that gonna
continue to evolve over the next few years?
And and what's the broader impact that you
foresee this having on on the industry?
So, I mean, again, I'm speculating just like
I'm guessing like everybody else. Sure.
Ambient
AI entered the scene
as an automation,
as in something that a human was doing
typing in a suboptimal
manner, or you might have had a human
Skype present.
And that thing is now
automated,
which
immediate return and ROI is the physician now
has the attention to spend on the patient.
So that's great check.
But now we're collecting a new kind of
data
in electronic form that we never had before,
the real time conversation
of what is happening.
Now many things can come from that.
This kind of
evidence generation on demand is sort of near
and dear to my heart,
but you can imagine other things
like documentation for billing could be completely automated.
This whole idea that a biller and a
coder has to now review everything and then
send a query back to the physician saying,
hey, doc. Do you remember? Was this the
case with the patient or not? That whole
thing can go away.
We might be able to produce
summaries
that are directed to the patient. So the
same note can be pushed through another kind
of summarizer,
AI that translate it to whatever language you
need and outlines the three things the the
patient needs to watch out for. So
I think it's gonna enable a whole new
class of care activities that we did not
think were even possible.
Absolutely. So it sounds like I mean, you're
predicting not even just, an improvement in patient
and and quality outcomes, but really massive repercussions
for the compliance and reimbursement perspective within the
health systems as well. So it's absolutely fascinating
to think about.
And even patient care. So, like, imagine if,
you know, you went to you went to
a physician
and they said, you know, watch out for
these symptoms
once every week.
If we recorded that conversation, we can trigger
an agent that checks in with you by
text, phone, or email or calling you saying,
hey, Jacob. Are you having this in the
past seven days?
We're never gonna do that manually.
Sure.
No. It's it's really exciting.
And and certainly,
I know here in in the Chicago area,
we've we've been seeing health systems, personally as
well, rolling out this technology.
And and it's, very clearly from the physician
perspective
already and in the data as well, reducing
that administrative burnout that that so many of
them face.
So really exciting things on the horizon
at Stanford as well in this atmosphere.
But before we go, Nigam, what else are
we missing? You know, you've got the ears
of a lot of other CIOs and and
health IT executives in health systems from around
the country. What else do you wanna share
with them about this this new pilot with
Atropos or or about, this technology that we've
discussed in general?
So I would like to sort of plant
this idea that one of our faculty members
here, Eric Brynjolfsson,
calls the Turing trap.
I'm sure every all the listeners, the everyone's
part of the Turing test where a computer
passes off as being a human.
The tutoring trap is the opposite of that,
where we as humans
only imagine doing with the computer what we
already know how to do.
So that's why we think about compliance and
billing.
But we don't follow-up with our patients
routinely as much as we do. We just
don't have the time.
But what if technology enables that?
And so the way I would encourage people
to think of it is that, you know,
AI writ large, or I like to think
of it more as a data plus algorithm
configuration,
it started out doing simple things, you know,
giving alerts and nudges and classifications
and predictions.
And then it matured into giving recommendations,
giving observational studies and bedside evidence.
Now it's getting
further advanced into actively listening and summarizing and
translating.
But what's next? What other actions can it
take? Can it do follow-up? Can it check-in
on your meds? Can it check-in on your,
glucose levels? Can the conversation
trigger an agent that checks in on your
CGM monitor and its values automatically.
So I think what we need to be
thinking about is what are the actions
that we should be taking but are not
yet taking
that can be done for cheap and fast
with AI.
Fantastic.
Great parting words for our listeners. So, doctor
Shah, I wanna thank you for taking the
time to sit down with us and for
sharing about all the impactful work going on
under your leadership at Stanford HealthCare. We really
appreciate it. Thank you for having me.
And to our listeners, if you'd like to
listen to more podcasts from Becker's Health Care,
you can visit beckershospitalreview.com.