Latest / Becker’s Healthcare Digital Health + Health IT / Scaling AI and Transforming Care Delivery at Cleveland Clinic with Dr. Rohit Chandra

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Scaling AI and Transforming Care Delivery at Cleveland Clinic with Dr. Rohit Chandra · Becker’s Healthcare Digital Health + Health IT

0:00
This is Laura Dierdahl with the Becker's Healthcare
0:02
Podcast. I'm thrilled today to be joined by
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doctor Rohit Chandra,
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chief digital officer at Cleveland Clinic. Rohit, it's
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a pleasure to have you on the podcast
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today.
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Pleasure to be here.
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Fantastic. And, you know, I'm excited to dig
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into our conversation because I'm looking forward to
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learning a little bit more about some of
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the cool things you are doing at Cleveland
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Clinic right now. I know you've got a
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lot, with AI happening, and it's just really,
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you know, important to have that perspective and
0:27
where you how you're thinking about the future
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too. But before we dive in, can you
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introduce yourself and just tell us a little
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bit more about what you're doing at Cleveland
0:34
Clinic?
0:35
Sure.
0:36
I'm Rohit Chandra. I'm the chief associate officer
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at the clinic. I joined the clinic about
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four years ago actually from outside of health
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care, so I'm a engineer by training.
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And in my role here at the clinic,
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I have responsibility
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for
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our technology footprint at the clinic.
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And the way to think a little bit
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about it is that technology is an critical
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capability
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for any industry, but for health care in
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particular.
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And so from that lens, I have responsibility
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for traditional IT,
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artificial health records,
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data,
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analytics,
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and increasingly,
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AI.
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That's fascinating to hear. And, you know, a
1:20
a huge job in front of you. And
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so I think if we narrow it down
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to just the last year or so, can
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you tell me a little bit about the
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projects or initiatives that had the most impactful
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results? What did you do, and how did
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you measure those outcomes?
1:33
So,
1:34
for 2025,
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I think,
1:38
for us and perhaps for many organizations
1:41
like us,
1:42
the single most impactful
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AI initiative has been the AI Scribe.
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And AI Scribe for the audience today, just
1:51
to simplify it, is
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all of us will often, as patients, go
1:55
to a physician for an outpatient appointment. And
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let's say it's a twenty or thirty minute
1:59
appointment
2:00
that we have, let's say, with our primary
2:02
care physician or some other physician.
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What we don't realize is that after that
2:08
encounter,
2:10
physicians typically need to spend anywhere from ten
2:13
to fifteen minutes writing up their clinical notes
2:16
from the encounter.
2:18
So primary care doctor today can spend anywhere
2:22
from
2:23
one and a half to three hours
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every day
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after their clinic time
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writing up their notes from each patient visit.
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Now this is essential work. It's really important
2:36
for continuity of care, for safety, quality, and
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everything else. So this documentation is a good
2:43
thing from every aspect. It's just that it's
2:45
very time consuming.
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So what we,
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what technology has increasingly emerged over the last
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year or
2:54
two is something that I refer to as
2:56
an AI scribe.
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So this is essentially
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you can just on your iPhone, you can
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have something that will listen to the provider
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patient
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conversation.
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It will
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record it,
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transcribe it, and then automatically
3:13
generate a draft of the physician notes and
3:16
the patient notes.
3:18
And what is amazing is the technology has
3:21
become so good that it can generate really
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high quality notes.
3:25
We still have a physician in the loop
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to ensure that they're complete and accurate,
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but,
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it can
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substantially
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reduce the documentation burden on physicians.
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And equally, if not more importantly,
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it can
3:41
significantly
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enhance the
3:44
patient provider
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interaction
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during the encounter.
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So today, very often, if you were to
3:50
go to your see your doctor, the doctor
3:52
is often sitting at the computer sometimes looking
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at you, but most of the time looking
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at the screen typing up their notes. Well,
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guess what? With this technology, they don't have
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to do that. They can have their full
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attention on the patient,
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and, the documentation
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happens almost magically.
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So I would say looking back to 2025,
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this has been
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a game changing experience in reducing the documentation
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burden,
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enhancing the provider experience, enhancing the quality of
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the provider patient interaction.
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We've been very successful at it. We've had
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more than 4,000 physicians adopted, and we've had
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millions of encounters that have now been
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documented
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leveraging AI as opposed
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to manually.
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So I would say looking back in 2025,
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that has been a big success story for
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us.
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That's great to hear. And I know that,
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you know,
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documentation technology and
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AI scribes,
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have a lot of value when it you
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it comes to supporting the clinicians and making
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sure that they're able to have more of
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that human to human interaction.
5:00
As you look into the next year, where
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do you see some of the big,
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priorities as well as headwinds that you're focused
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on for 2026?
5:09
So we're looking at bringing technology
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in both
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clinical and nonclinical
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domains or operational areas.
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I think just building on the Scribe conversation
5:21
we just had,
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we think that the potential for AI
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to build significant that the Scribe is just
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the beginning.
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We think that there are so many more
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powerful capabilities
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that can help a physician
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in delivery of care. It can be
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summarization
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summarization of a patient chart before an encounter.
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It can be engaging with the patient chart
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in a much more conversational and intelligent way.
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All of these are capabilities that will significantly
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provide assistance to the provider at the level
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during a patient encounter. So those are areas
6:00
where we are trying to enhance and build
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upon
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the AI scribe, which we think is just
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a starting point.
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Other areas are, I'll say, more in the
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back office space. We've done good work over
6:12
the last year in bringing AI into coding
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automation.
6:16
That has been pretty helpful for us and
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has potential to significantly
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accelerate that work for us going forward.
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We've also
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started to use AI and machine learning in
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clinical risk prediction. So we launched something to
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do sepsis prediction last year
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that was very helpful.
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Sepsis, just for our listeners,
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is a
6:41
a bloodstream infection that is very complex to
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detect
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but and has very high mortality rates.
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And so with AI, we can we built
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a system that does early detection of sepsis
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and that can significantly
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improve our ability to intervene early
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in patients that show the onset of sepsis.
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When I'm going with this is all of
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these are areas
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beyond just the AI scribe
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where we think we have significant potential to
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apply AI powered capabilities
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in other aspects of health care developing.
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That's helpful to understand and really cool to
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think about the technology and and implications on
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how it can augment it and boost the
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quality of care you're able to deliver and
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especially with early detection on something like sepsis,
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I know is, life saving.
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I'm curious. When you look at bringing in,
7:33
more of the technology into the clinical setting,
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how do you think about,
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governance and monitoring and just making sure that
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AI stays on track and is giving you
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the results that you need? I know it's
7:44
not always just, kind of implementing the technology
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and and letting it go, but truly making
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sure that over time, it's still performing at
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the rate you want it to be performing.
7:54
Oh, I think, Laura, you're exactly right, which
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is
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health care
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is complex.
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It's
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tech and technology is only an
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enabler.
8:07
It's very important for us to work
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super closely and in deep partnership
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with the clinical leaders at the clinic to
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ensure that this is helping with health care
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development.
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The second aspect that I think is really
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important is,
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you know, AI is in its early stages,
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so it's important for us as we navigate
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these waters to do it as safely and
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as responsibly as possible.
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And so we always have a human in
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the loop so that, you know, the technology
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is helping assist.
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But, you know, the technology is never perfect.
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And so,
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we just need to be very thoughtful about
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what are the areas that we bring AI
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to bear and how do we apply,
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apply it in a way that has a
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human in the loop so that we can
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be safe and responsible.
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So that's something that's core to us when
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we
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look ahead,
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in terms of bringing AI and applying it
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to other areas that I just touched upon.
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That makes a lot of sense, and it's
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helpful to understand how you're thinking about those
9:11
things as I know it's a such a
9:13
fast evolving field.
9:15
What do you think the hardest thing you'll
9:17
have to do in the coming year will
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be?
9:20
I think the
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main
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opportunity
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ahead of us
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is really
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how do we scale up our
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ability
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to
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not just the two or three things that
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I mentioned,
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but 10 or more different initiatives across the
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organization. And expands,
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we need to expand our ability to mobilize
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ourselves as an organization
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to
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pick and choose the right problems,
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mobilize the technology teams, move the clinical and
9:56
operational leaders
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to say, what are the problems that we
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want to go after? How do we
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work with
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our technology partners to properly build the right
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solutions?
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And as I mentioned,
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bringing an air in a clinical setting is
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hard work.
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We need to make sure that we are
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not bringing in just technology, but we're doing
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this in a way where we cannot just
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apply it safely, but, you know, apply it
10:23
at scale and stay with it till it
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is deployed enterprise wide. So, actually, the change
10:29
management is
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essential
10:32
to do carefully and responsibly. That is actually
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as big a lift as the technology.
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And looking ahead,
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I think it's really twofold.
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Let's make sure that we're picking the right
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areas to apply it to. And then number
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two, to be able to do these efforts
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at scale with
10:51
that carries the organization and carries health care
10:53
forward.
10:55
That makes a lot of sense. It's helpful
10:57
to think about it in that way. I
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mean, I know, you know, building a team,
11:01
that can be nimble and,
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ready to,
11:05
you
11:06
know, incorporate technology is
11:09
easier said than done in many ways. And
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so when you look at the culture at
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Cleveland Clinic, I know it's a special place
11:15
and certainly has been an innovation leader for
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a long time.
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What do you do to make sure that
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your teams are,
11:22
you know, ready to
11:24
to go with the AI technology and are,
11:27
staying innovative while also staying safe?
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So it's a work in progress. I think
11:35
that
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all of us, as well as folks,
11:40
in other industries, are
11:42
grappling with how to bring the right technology
11:44
and tools into the organization,
11:47
how to give ourselves a safe space to
11:49
explore, to experiment, to learn.
11:52
So I think that,
11:55
it's a journey.
11:57
A few things that we have done
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are we hired a chief AI officer,
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eighteen months back from outside the industry,
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and we are increasingly building up a team
12:10
of folks who have background in technology.
12:14
And
12:15
the approach that we're taking is
12:19
how do we
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build
12:22
deep relationships and partnerships with clinical and operational
12:26
leaders at the clinic
12:27
so that we can,
12:30
bring a technology first mindset to many of
12:32
the important areas within health care. That's the
12:35
opportunity that I think is ahead of us.
12:37
AI is a very powerful tool,
12:40
but in our business,
12:41
how do we apply it in a health
12:44
care setting is the challenge and the opportunity.
12:47
So bringing in fresh talent is a thing.
12:49
Picking and choosing problems is a thing. Giving
12:52
ourselves a safe space to experiment, to try,
12:55
to learn as we go along.
12:57
These are all the
12:59
ways in which we are
13:01
increasingly activating our organization
13:04
to be,
13:06
to apply AI to health care.
13:09
That's helpful to understand and, you know, really
13:11
a great approach,
13:13
in strategy, it seems like, in order to
13:15
be ready for whatever comes next. And, you
13:18
know, speaking of that, how do you see
13:20
health care changing and shifting and evolving because
13:22
of the technology and because of AI as
13:24
it's coming more rapidly into the space? Where
13:27
are those some of those opportunities that you
13:29
see and are excited about for 2026 and
13:32
beyond?
13:33
So let me actually look at it,
13:37
from a, perhaps, a multi year perspective.
13:41
I think
13:43
that
13:44
I approach it both from
13:49
a operational as well as a clinical setting
13:51
where I think over time,
13:54
each of these areas have the potential to
13:56
be substantially
13:58
transformed using AI.
14:00
If you just look at the from an
14:03
operational lens,
14:04
we are a complex organization
14:06
that does more than 30,000
14:09
patient encounters a day.
14:12
Every patient encounter
14:14
requires the coordination
14:16
of
14:17
an exam room, an operating room, providers,
14:20
nurses, physician assistants.
14:22
So we have to coordinate the activity
14:25
of tens of thousands of
14:27
caregivers
14:29
and orchestrate
14:30
30,000
14:31
encounters.
14:32
But guess what? For every patient, it's perhaps
14:35
the most important
14:37
event of the day, and we need it
14:39
to go perfectly.
14:41
So this is an area where we're using
14:43
technology to help do the coordination of all
14:46
of these activities
14:47
so that they work like clockwork.
14:50
We want technology and algorithms to automate
14:53
to and to optimize
14:55
these encounters.
14:56
The benefit over time for the industry is
14:59
we should be able to do our activities
15:01
more efficiently
15:03
at greater scale and give us the ability
15:05
and maximize our ability to see as many
15:08
patients as we can. So this is an
15:10
example of an opportunity over time in an
15:13
operational setting.
15:16
In
15:17
a couple of examples I'll give from a
15:19
clinical setting perspective,
15:21
we know that,
15:22
health care is a very
15:24
labor intensive industry,
15:26
and our ability to deliver care is actually
15:29
limited by our capacity
15:31
by providers and nurses.
15:33
At the same time, providers and nurses are
15:36
overworked,
15:37
and there's significant burnout.
15:40
So there's an opportunity to say, how can
15:42
we bring technology
15:43
to substantially
15:45
transform and ease
15:47
the ability, the efficiency, and the experience of
15:50
our caregivers.
15:51
And ASRIVE is an example that I just
15:53
gave, but,
15:55
over time, I think all of these technologies
15:58
have significant potential to reduce the
16:02
busy work that caregivers go through. This is
16:05
good for doctors. It's gonna be good for
16:07
nurses, and it is going to be good
16:09
for patients because it will expand our ability
16:12
to deliver care.
16:14
The third area I'll touch on over time
16:16
is just improving
16:18
patient clinical care and safety.
16:21
Having technology
16:24
that can, let's just say, do risk prediction
16:27
and risk stratification of patients
16:30
helps us focus our energy on the patients
16:32
who are at highest risk for adverse
16:36
symptoms and consequences,
16:38
and that helps us focus our clinical energy
16:41
on the patients that need it most. So
16:44
this will help us actually take better care
16:46
of patients. So those are examples
16:48
where we think
16:50
we can substantially
16:52
improve
16:53
the quality and the safety of the care
16:55
that we deliver to our patients.
16:57
So if I step back over time,
16:59
if I look at
17:01
the macro landscape,
17:03
I am a deep believer that AI can
17:06
help us make health care safer,
17:09
cheaper,
17:10
and more scalable.
17:13
I love that. Roa, thank you so much
17:14
for joining us on the podcast today. This
17:16
has been a a really fun and fascinating
17:18
conversation, and I look forward to seeing you
17:20
as well in April. I know you'll be
17:22
speaking at our annual meeting, and doing a
17:24
fireside chat, which will be amazing to dig
17:27
deeper into many of these themes and and
17:29
just the things that you're doing at Cleveland
17:30
Clinic. So I appreciate your time today and
17:32
look forward to seeing you in a couple
17:34
months.
17:35
Thank you so much, Lauren.