- Hello everyone, this is Jacob Emerson
with Becker's Healthcare.
Thanks so much for tuning into the Becker's Healthcare
podcast series where today I'm very pleased to be joined
by Akiles Bpu, who is founder and CEO at Deep Scribe.
He's here with me today to talk a little bit more about why
AI note customization is key
for widespread clinical adoption at large
healthcare organizations.
So Akiles, thank you so much
for taking the time to be with us today.
- Thanks, Jacob. I'm super glad to be here as well.
- Yeah, we're glad to have you with us.
And, and to get us started,
I'm wondering if you could tell us a little bit more about
yourself, your background in healthcare
and what it is that you do today at Deep Scribe.
- Yeah, absolutely. So prior to Deep Scribe,
I was actually exposed to the problem
of documentation at a pretty young age.
You know, my dad was an oncologist
and as a son of an oncologist,
I actually would say I was desensitized
to the problem of documentation.
You know, he would spend upwards of half his day writing
notes on everything his patient said to him,
and a lot of that he would bring home.
But as a kid, not much you can actually do about it.
So I admitted defeat and a few years later,
or roughly decade later,
I eventually found myself at Bear,
which is Berkeley's AI Research Lab.
And I was working on summarization at the time.
And you know, back in 2017, summarization
was starting to do amazing things, including being able
to distill one to two page CNN articles into nice,
concise abstracts.
It was pretty amazing. But every time I traveled back home,
it was like I was going 10 years into the past.
My dad was using the same tools, the same tech,
and spending the same amount of time on documentation.
And so I started to ask the question, you know,
why not build a tool that could listen
to my dad's conversations and summarize them
and fill out his documentation for him?
And if I were to do so, would
that actually save him the time he
was spending on documentation?
Because obviously it meant a lot to me.
So I took the same first
principles approach we did in the lab.
I tried to collect the data and then curate it
and then train a model on it.
But the data step is where I got stuck.
There was no data set of patient physician conversations,
there was no data set
of the transcripts, the annotated notes.
And so it was virtually impossible to train an AI to,
to do this work at the bar of accuracy
that's required in healthcare.
But I think what was more interesting to us
as technologists was that
these conversations
were actually healthcare source of truth.
So every patient's story started from one
of these interactions and no one was actually
collecting this data.
So, you know, we thought that if we could collect this data,
not only could we solve documentation,
but we could start to solve some
of the larger problems in medicine.
And so we started Deep Scribe to do exactly this.
So we launched the product in 2020
and we quickly became one
of the market leaders in documentation.
So today Deep Scribe is deployed in over a thousand provider
organizations, everything from a private practice
to a larger health system.
And you know, on average we save about three hours a
day for each of our doctors.
But I think the most important thing to us is
that we've been able to do this across a diversity
of workflows and specialties.
So when you see digital health products deployed, typically
a lot of groups of clinicians are left out, you know,
whether it's the specialists or whether it's the inpatient
docs, because their workflows are highly nuanced
and diverse to the point that the product can't fit them.
However, with Deep Scribe, by deploying the primary care
or private practices early on
and seeing thousands of different workflow permutations,
we were able to build what we call customization studio
that allows any provider to fine tune deep scribe
to fit whatever workload they had.
So we've actually, by doing this, found
that this is the single biggest key to adoption of any sort
of AI in healthcare.
- It sounds like this is a very personal passion for you,
uh, on top of being obviously a professional one
as well, Quiles?
- Yeah, absolutely. I mean, I think one of my favorite parts
of the whole Deep Scribe journey is
that my dad actually uses Deep Scribe today,
and then he's one of the clinicians that actually is able
to save multiple hours in his
day now because of Deep Scribe.
So it came full circle and,
and you know, I know I built it for my dad initially,
but it's great to have other doctors be able to have access
to this technology as well.
- Sure. Yeah. No, it's very cool to hear.
And I do want to get deeper into what's,
what's the latest on clinical note taking, but,
but I do wanna stay, take a step back for a second
and ask you a little bit about a trend we've been seeing
over the last few years, uh, just this rise in hospital
and health system leaders,
investments in artificial intelligence technology across the
board, not just in clinical note taking.
Yeah. So based on your experience ish,
how are you seeing organizations deploy AI the most, um, or,
or to support clinical documentation?
Uh, are you seeing key challenges that they're looking
to address ultimately?
- Yeah, absolutely. So it's been amazing from our vantage
point because when we started the company in 2018, we got
to see sort of this adoption curve go from
no one is really looking
and talking about clinical documentation
to now everybody is talking about clinical documentation.
So in the beginning, you know, we actually made the decision
to serve deep scribe to only private practices
because the sales cycles
and the implementation cycles
that systems were just unsustainable for us
as a, as a startup.
And, you know, the key things there are just accuracy and,
and proof points that tech would actually, you know,
solve the challenges that the health systems are looking at.
But over time, as we've built up a name
for ourselves in the private practice industry,
we've started to see that, you know, as the AI kind
of thought space has evolved, a lot
of health systems are now finally looking at, uh, AI
as something that's here to stay.
And so I think if you look at the numbers, you know,
earlier this year there's a study done, um, that share
that about 30%
of systems are currently looking at documentation.
And now I think, you know,
the latest studies show something closer to 60%.
So it's been amazing to see health systems kind
of embrace AI.
And you know, at the center of it, I think is the challenge
of physician burnout.
Um, I think that's what a lot of these systems are,
are looking to solve with,
with documentation tools like Deep Scribe.
And I think that sits at the center
of generative AI in healthcare for a couple reasons.
You know, the first, I think there have been companies like
Deep Scribe that have been building solutions
and building the workflow enhancements
and integrations, so that come time to implement,
they're ready to go with all
of the surrounding functionality.
And now you, you have Cort Tech that's finally mature,
so now you can actually deploy these mature solutions.
But I think the second thing is that, um, it's really easy
to see the value from these solutions.
So, you know, with Deep Scribe, for example, as soon
as it's implemented within a few hours,
you're seeing clinicians actually start to save time
and send, you know, little notes to administration that,
you know, software IE deep scribe has been amazing
because now they don't have to focus on notes anymore.
They can now focus on their patient.
Some physicians are that have been scaled down to just two
to three days of, of practice from the full four
or five are now going back to the four or five
because of these documentation tools.
So I think, you know, you're able to see instant value
and, you know, I think the tech is mature enough for,
for this specific space,
which is why I think organizations see this as the,
the most obvious first step in their generative AI journey.
- And you mentioned a, a jump from 30 to 60%
or so of systems now looking into AI
powered clinical documentation.
What advancements do you attribute that to in terms of
where clinical note taking has
improved over the last few years?
And are, are you finding that customization fits in here?
Um, why is all this significant, I guess is
what I'm asking, Ailish?
- Yeah, I think that's a good question.
So, you know, since the beginning
and still now, I think the challenges
to adopting a technology like this have been the same.
The two biggest are trust and customization
and trust in our world is akin to accuracy.
So if those are the problems, I think in terms
of advancements, a lot of people see that the advancements
for accuracy have been primarily tailored around GPD four
and the advancements in foundation models.
But we actually see that the biggest advancements were in
open source lms.
So, you know, while GPD four is incredible,
without the right checks
and balances, that mislabel a medication
almost every single time.
So this open source of movement has been amazing
because as you collect a small amount
of domain specific data
and train these alums on it,
you can quickly become on par with GPD four.
But with large amounts like we have, you can actually
solve problems like hallucinations or misinterpretation
or other common mistakes fairly effectively.
So I think that's the first big advancement is
that trust has just come a long way
and accuracy has come a long way to,
to finally meet that bar.
But the second thing that I think you mentioned was
customizations, and I think that's probably
the other large advancement.
And so the product, in order
to get full widespread adoption, can't just work for
that one clinician that gets all their, uh,
information from the conversation.
They're okay with unstructured notes.
It also has to work for, you know, the oncologist
that gets most of their node from previous visits,
or the highly specific geriatricians that want the node
to follow exact structure that they've been using
for the last like 10 years.
So, um, I think the, the second big advancement
that we've seen is, you know,
given the advancements in foundation models
and open source lms, we can now evolve that
to customizations.
And Deep Scribe has embraced that
and released something called Customization Studio
that is the culmination of all these little things
that clinicians have asked us
for over the last five years wrapped into this,
I guess, seamless interface.
And you can kind of think of it as clinicians now being able
to build their own custom model in a few minutes.
So when it comes to adoption, now you have trust
that's relatively solved as well as customizations
that's also solved that are the two keys
to just rolling out something like deep scribe
system wide. I
- Wanna follow up with you on, on the trust aspect of this
because I think when we're talking about, uh,
implementing AI tools within healthcare, the other half
of this conversation has to be around, uh,
the responsibilities around using this technology.
And we know the healthcare industry is working
to set guidelines right now,
this year they're being released around the use
and development of these technologies given the, the,
the range of concerns around accuracy, privacy, uh,
potential biases and discriminatory concerns.
And so I would ask you what considerations,
what best practices do you recommend for,
for leaders in charge of these tools as they explore,
as they begin to implement these tools
and like what you mentioned as they begin
to customize these note taking
tools that we're talking about?
- Yeah, absolutely. I mean, I think there,
there are three things that I would highlight
as very important to, to look into and, and get right
before adopting generative ai.
I think the first thing is everybody should do their own
benchmarks or at least develop their own
benchmarking methodology.
And in the, you know, AI documentation space, uh,
especially, you know,
you can plug in a conversation into GPD four
and have a note that comes out
that looks like it would work.
But when you examine it closely,
you'll see hallucinations happen.
You'll see, uh, things like misinterpretation
or miscategorizing
or mislabel that can have, um,
pretty significant downstream effects.
So having a di having a benchmarking methodology would help
a system, you know, prevent against that.
And, and we have our own a deep describe that we're happy
to share with anyone that's, that's looking at this.
But I think that's the, the first thing
to, to catch mistakes early.
The second thing is human oversight.
So, you know, and you can even do, you,
you can do this even if the solution is fully automated.
So deep describe, for example,
our solution's fully automated,
but we have a team of folks internally
that review at random different notes to ensure that
there are no hallucinations that are currently being missed
by, uh, clinicians or misinterpretations
or, or other errors.
So that's, I think the second thing that's important.
The third thing is automated oversight,
and we've done some work here as well,
but you can actually configure LLMs to be able
to catch their own mistakes by having one
specific dedicated reviewer, LLM to to review the notes,
compare it with the transcript,
and see if there's anything, uh, that's been missed.
So I think those three things I I would say are,
are probably the most important as it pertains to,
to guidelines and, and, and safe AI development.
- Yeah. Well, I appreciate you taking us through that
and offering all those details.
The last thing I wanted to ask you about achiles
is on the ground.
I know you, you mentioned your father
and how he's been using this tool in oncology
and some other physicians you know, as well.
But can you share some, some on the ground examples
or some case studies of where we are seeing hospitals
and health systems deploy AI for customized medical notes
and ultimately the outcomes that you, that you're seeing
and that they are seeing from that?
- Yeah, absolutely.
So one of my favorite case studies
is Covenant Healthcare.
So Covenant Healthcare has used Deep Scribe
for actually a little bit less than a year.
They're on Epic.
And, um, within six months, they were able
to not just reduce documentation time
by roughly 53% across all their providers,
but they were able to see a 72% reduction in pajama time.
And that's my favorite stat
because, um, a lot of the work
that clinicians do ends up being carried home
and being able to reduce that by three fourths.
So, you know, if they're spending an hour
or a day on it in the evening, now they only have
to spend 15 minutes, I think is a big, big win.
Second big win is that, you know, even though we like
to say we're able to save clinicians' documentation 80, uh,
reduced documentation by 80% in practice
because of the diversity of clinicians
and their workflows, um, we've been able to show
that we were able to do that by 53%
consistently across everybody.
So regardless of if you're, you know, the hyper picky doc
that wants their documentation a certain way
or specialist that has a highly configured workflow,
we're able to make an impact, um, for, for everyone.
So I think that's awesome to see in,
in the last great tidbit that I wanted to insert there from
that study is that they actually just in six months made the
call to replace their virtual scribe program just months
after implementing the scribe.
So I think that's a great one that shows some
of the value you can get from
generative AI and documentation.
- Yeah. And those are some incredible metrics you shared,
especially that 53% reduction in documentation across
specialties across all kinds of physicians.
That's, that's amazing. Um,
before we go though, Aish, any other final thoughts or,
or key takeaways you would like to share with all
of our health system listeners, uh, listening in today?
- Yeah, absolutely. I mean, I think we're about to roll in
to the biggest year for Gen AI in healthcare yet,
and I think everybody is extremely excited, um,
whether you're a startup
or, uh, that's, that's building a solution for this
or health system that that's, that's integrating it.
But I think the two things to really look out for our trust,
so, you know, how accurate is the ai,
what benchmarking methodology does this company use in the
backend to verify that, you know,
in production it'll be reliable and and trustworthy.
And the second thing is customization.
So how can the AI adapt to all the, the big, I guess,
diverse amount of workflows that your clinicians have
and how does the product account for that?
And I think those are gonna be the two most important things
to get the adoption that everybody here is looking for.
So, you know, we've done a lot of work on this and,
and happy to share notes with anyone interested,
but I think those are the two things
that we predict are gonna be very important.
And so I'm super excited to, to see this year play out and,
and see just how much AI is gonna really impact and,
and improve healthcare outcomes.
- Fantastic. Well, Aish, thank you so much
for taking the time to be with us
and for sharing your insights
with us. We really appreciate it.
- Yeah, thanks for having me. Jacob.
- We'd also like to thank our podcast sponsor, deep scribe,
you continue to more podcasts from Becker's Healthcare
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