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Rethinking Hospital Operations with a Central Nervous System Approach · Becker’s Healthcare Podcast

0:00
Hello, and welcome to the Becker's Healthcare podcast.
0:02
I'm Molly Gamble. Thanks for joining me. Few
0:04
questions are more pressing for hospital leaders right
0:07
now than this one. It's not just how
0:08
to manage through today's environment,
0:10
but how to fundamentally rethink the way a
0:12
hospital runs.
0:14
My guest today has spent the last decade
0:16
and a half on exactly that. Mohan Giridharadas
0:19
is the founder and CEO of Leentas, a
0:21
health care AI and analytics company that works
0:23
with many of the country's leading health systems
0:25
on capacity, staffing, and patient flow. Mohan, thank
0:28
you so much for joining me today. Thanks,
0:31
Molly. Always good to chat with you.
0:33
It's great to be with you. In in
0:35
this conversation today, we're gonna be digging into
0:37
what a modern hospital operating model actually looks
0:40
like and why Mohan believes the next leap
0:42
forward isn't about automating existing workflows, but building
0:45
something closer to a central nervous system as
0:47
he describes it for hospital operations. We're gonna
0:50
get to that in a moment. But, Mohan,
0:52
maybe first we can establish the stakes here.
0:54
Obviously,
0:55
health care leaders are dealing with rising demand,
0:57
staffing constraints, policy changes, more and more financial
1:01
pressures all at once.
1:02
From your vantage point working with 200 systems,
1:06
what has fundamentally changed in how hospitals operate
1:09
today? What feels different now even compared to
1:11
five years ago?
1:14
Molly, as I think about the last year,
1:16
most of last year, there was a bit
1:18
of a freeze with all the new regulations
1:20
coming out of Washington.
1:23
I think, you know, with the tariffs and
1:25
with research grants and with Medicaid policy changes,
1:28
etcetera,
1:29
I think we've settled into a new normal,
1:31
but the new normal is not an easy
1:33
new normal. I think health system leaders have,
1:36
you know, fully absorbed that. The demand continues
1:39
to be high,
1:40
labor continues to be constrained,
1:43
and reimbursement pressure will continue. None of those
1:45
feel temporary.
1:47
And so that's the new normal that health
1:49
systems need to
1:50
operate against.
1:51
Now in the
1:53
past, health systems could build their way out
1:55
of problems by adding beds, adding ORs, hiring
1:58
staff, putting in new hospitals.
2:00
And what capacity does
2:03
is it masks the underlying problem because
2:06
as long as you've got plenty of capacity,
2:08
a dashboard showing you how yesterday did was
2:10
good enough to run the operations.
2:12
But now that the slack has gone,
2:14
what we are seeing is persistent gridlock.
2:17
If you talk to any health system leader,
2:20
they wake up every day to this pretty
2:21
much the same set of messages. The ED
2:23
boarding is high. The census is gonna be
2:26
high. It's taking three or four hours to
2:27
get a bed. Its discharges are getting delayed.
2:31
And that's
2:32
the problem.
2:33
Now if you go back to what you
2:35
said, what is different over the last five
2:37
or ten years,
2:39
the reality is clinical complexity
2:41
today is massively higher than it was five
2:44
years ago, ten years ago, fifteen years ago,
2:46
just massively different.
2:48
Meanwhile, the operational sophistication
2:52
has not really changed that much. The same
2:54
dashboards from the same EHR companies
2:57
plus spreadsheets and human middleware
2:59
is how most health systems operate.
3:01
And that's why we're seeing what we're seeing
3:04
because health systems are running
3:07
a very complex
3:08
capacity constrained systems
3:11
using tools and methods designed for a different
3:13
era.
3:15
So it sounds like the the clinical complexity
3:17
line has just
3:18
really skyrocketed in terms of being massively different,
3:21
and then the operational sophistication has remained
3:24
plateaued. That has not budged
3:26
as you described it, Mohan. Given
3:29
that shift over the last five years, what
3:31
what do you think a modern hospital operating
3:34
model should actually look like?
3:37
When I think about what is the starting
3:39
point, the starting point is recognizing one fundamental
3:43
truth.
3:44
Health care is an asset intensive
3:47
service delivery.
3:48
Of all the kinds of service operations,
3:51
asset intensive service businesses are the hardest to
3:54
optimize.
3:55
Why? Because you've got very expensive assets
3:58
that typically are purpose built. They have a
4:00
very specific purpose,
4:02
and they have very specially trained people to
4:04
operate it. Think about an imaging machine or
4:06
an OR.
4:07
Not anybody can come in off the street
4:08
and learn how to operate those things. And
4:10
so delivering any service
4:13
requires that you line up the asset, line
4:15
up the right staff with the right skills,
4:17
in the right room, with the right patient,
4:20
and the right supplies.
4:21
Otherwise, the service can't happen if any one
4:23
of those things falls apart.
4:26
That's what makes asset intensive service operations particularly
4:29
complex. That's why calendar gazing and appointment making
4:32
simply doesn't do it. Now when I think
4:34
about
4:35
what else is an example of an asset
4:38
intensive service business, it would be airlines,
4:41
Delta, United, American,
4:43
or logistics companies, UPS, FedEx, etcetera.
4:46
Now when you think about those industries, airlines
4:48
or logistics,
4:50
they've invested
4:51
billions of dollars and several decades
4:54
building sophisticated
4:56
capacity
4:57
flow, yield optimization,
4:59
asset turns, asset,
5:01
velocities, those sorts of things
5:03
to their operations.
5:04
Healthcare has not done those things. So when
5:06
you look at a very simple metric,
5:08
operating margin
5:10
divided by the value of the operating assets,
5:13
health care is an order of magnitude
5:15
lower than airlines or logistics.
5:18
Now in fairness, planes can fly all night.
5:20
Health care can't do surgeries all night. So
5:22
by definition, it's a bit of a one
5:24
arm tied behind your back problem.
5:27
But
5:27
health care is also focused on clinical excellence,
5:30
which by the way, is the right thing
5:31
to focus on. No patient is gonna say,
5:33
you know, the surgery
5:34
wasn't effective, but the operational experience was better
5:37
than a Disney cruise, so I'm good. They're
5:39
gonna want the clinical outcomes to be correct
5:41
first.
5:42
And so
5:43
that's the challenge we've got.
5:45
If you go back to what you said,
5:47
what does a modern,
5:48
operation look like?
5:50
It requires continuous prediction, not a dashboard about
5:53
yesterday, but a prediction about the rest of
5:55
today, tomorrow, this week, next week. It requires
5:58
continuous optimization
5:59
because you gotta do the asset, the staff,
6:01
the room, etcetera.
6:02
It requires continuous adjustment.
6:04
It cannot be done through manual interventions or
6:08
frontline heroics.
6:09
So what we're leading to is what we
6:11
call a central nervous system,
6:12
meaning a system that can sense what's happening,
6:16
understand it and interpret what's happening,
6:18
take actions,
6:20
and then learn to get better, over time.
6:22
That's kind of what we are describing.
6:25
I appreciate you defining what that is because
6:27
I think to your point, Mohan, you walked
6:28
us through whether it's the
6:30
cruises or the airlines or logistics companies.
6:33
A lot of parallels or comparisons for health
6:35
care only go so far. So it sounds
6:38
like the central nervous system is really a
6:40
more,
6:41
transferable way to think about how a health
6:43
system in modern times operates.
6:46
Yeah. It that yeah. That's that's exactly right.
6:48
So describing it as a system that senses,
6:51
understands, acts, and learns. So let's make a
6:54
take a simple example.
6:55
Imagine you touch a hot stove. What happens?
6:59
Your fingertips sense the heat.
7:01
You understand
7:02
instantaneously
7:03
that that was probably not a good thing
7:05
to do.
7:06
Before your brain even processes it, your spinal
7:09
cord issues the reflex motion to pull your
7:12
hand away.
7:13
And then a second or two later, your
7:14
brain processes it and says, that really wasn't
7:17
great. I'm gonna learn never to touch a
7:19
hot stove again. So if you think about
7:21
what happened, you sensed, you understood, you acted,
7:24
and you learned
7:26
all in the space of a second. So
7:28
if you took each of those concepts and
7:30
applied it to health care, let's start at
7:31
sense.
7:32
SENSE is gathering input that fingertips
7:35
sense that it was hot.
7:36
Hospitals generate
7:38
thousands and thousands of sensory signals
7:41
every day. Right?
7:44
But they throw away 99% of it. It's
7:46
written on post it notes. It's device details
7:49
that nobody recorded. It's stuff on a spreadsheet.
7:52
It's stuff on an EHR dashboard that no
7:54
one looked at. So all these signals
7:57
are fragmented,
7:58
delayed, not captured, or thrown away.
8:01
That is kind of step one. It's like
8:03
going through the world, you know, with your
8:05
eyes closed and your ears shut because you're
8:07
shutting off sensory signals that you should have
8:09
captured.
8:10
So what do we need to do? We
8:11
need to capture those signals, filter out the
8:14
noise. We can't overreact to everything that happens.
8:16
Your fingertips
8:17
would not have freaked out if the stove
8:19
hadn't been on, and it had been cold.
8:21
So you've got to filter out the noise,
8:23
and then you've got to understand it.
8:26
So understanding has
8:28
operational understanding, meaning all the queuing theory flows,
8:31
bottlenecks, etcetera. So it's mathematical sophistication on operations,
8:35
and then clinical sophistication
8:37
on acuity is what's happening, what tests,
8:40
etcetera. And you need both of those.
8:42
And then you've got to act.
8:44
Take the necessary steps, whether they're operational steps
8:46
or clinical steps, and then learn
8:49
so you get better. So what does this
8:51
look like in practice?
8:53
A patient presents at the ED.
8:55
Imagine based on that initial encounter, you could
8:58
have predicted
8:59
this patient's gonna need a chest scan and
9:01
is gonna need a bed in the pulmonary
9:03
unit.
9:04
That's the sensed it, understood it. Now you
9:07
start acting.
9:09
Open a slot on the CT on the
9:10
imaging. Send messages to the right unit. Get
9:13
stuff started.
9:14
By the time the patient flows through it,
9:17
that's happened. Now if we guessed wrong
9:20
and it wasn't a CT, it needed to
9:22
be something else, and it wasn't a pulmonary
9:24
bed, it was a bed somewhere else,
9:26
then over time, the algorithm learns.
9:28
And the next time it predicts better and
9:31
learns better.
9:32
So this is how a system would adjust
9:34
and absorb and learn data, and that's
9:37
the the the notion of how you take
9:40
a central nervous system concept
9:42
and make it into an operational reality.
9:45
Mhmm.
9:46
Let's add AI now into the fold.
9:49
You mentioned health systems
9:51
asset intensive
9:53
services. Sometimes most often, the hardest to optimize
9:56
as you had said, Mohan.
9:57
I'm I'm curious now about how this is
10:00
playing out with the automation options available at
10:03
health systems through technology. You're hearing so much
10:05
about AI.
10:06
But, in your
10:08
in your expertise, what really distinguishes
10:10
a system that is
10:12
functioning like the central nervous system, so it's
10:15
sensing, adapting, learning,
10:17
versus a system that's just automating pieces of
10:20
existing workflows.
10:22
So a couple of things. I think what
10:24
makes health care complicated
10:27
is that the last mile is incredibly
10:29
hard.
10:30
If the scheduler doesn't put doctor Smith
10:34
with the patient John Doe in 0 R
10:37
2 at 09:30AM
10:39
with the right team of nurses, anesthesia techs,
10:42
the robot, and the implant vendor delivering the
10:44
artificial knee, that's actually won't happen.
10:47
You can't vibe code your way to that
10:50
and expecting AI to solve
10:53
thousands and thousands of these last mile problems
10:55
that happen everywhere.
10:56
So that's not gonna happen.
10:58
If you just blindly automate, you just make
11:01
a bad process faster.
11:03
That's all that automation accomplishes.
11:05
So part of what the central nervous system
11:07
needs to do is make the workflows more
11:10
intelligent,
11:11
automate where you can,
11:14
offer
11:15
assistance
11:17
by surfacing things. For instance, nurses are expected
11:19
to have read a 20 page medical record
11:21
before a patient walks in, and they had
11:23
thirty seconds of time before they picked up
11:25
the patient. There's no chance they're reading all
11:27
of that. So intelligent assistance, imagine if they
11:30
could read all of that and surface the
11:31
two or three things you need to do.
11:33
So there is the workflow. There is the,
11:36
assistive support. There is agentic support. There are
11:38
some tasks that are low risk that can
11:41
happen automatically.
11:42
At the same time, you need the human
11:44
in the loop to make the last decision.
11:47
And so
11:48
all of this forms a very tight control
11:51
system
11:52
that can be
11:54
governed with intelligent AI to do the last
11:56
mile. Let me give you another,
11:59
example that we can bring this to life.
12:01
Way back when before 09:11,
12:04
I was on a Lufthansa flight Singapore to
12:07
Frankfurt, fourteen hours, middle of the night,
12:10
and I got upgraded. So I was happy
12:12
with that. Turns out this pilot liked to
12:15
welcome
12:16
passengers
12:17
from the upper deck into,
12:19
the cockpit one at a time. And so
12:21
the moment I saw that happening, I put
12:23
my hand up and told the flight attendant
12:24
I'd love to be, you know, invited back
12:26
whenever the pilot is up for it. And
12:28
sure enough, an hour or two later, they
12:30
pulled me in
12:31
and I wasn't sure what to expect. I'd
12:33
never sat in a cockpit of a of
12:34
a seven forty seven in the middle of
12:36
the night, and
12:37
it was fascinating.
12:40
Still completely dark. The pilots were bored out
12:43
of their minds. That was the first reaction
12:45
I had. They were just so happy to
12:47
have someone to talk to. So both of
12:48
them are turned around looking at me and
12:49
chatting.
12:51
And I was fascinated looking at all the
12:53
dials and looking out the windows, etcetera. And
12:55
then I finally said, how come you guys
12:57
don't fly the plane? Wouldn't it be a
12:58
lot of fun?
13:00
And,
13:01
what he said has stuck with me.
13:03
The pilot said, sure. I could,
13:06
but the autopilot can fly it better than
13:08
I can. So I said, really, why is
13:10
that? He said,
13:11
it's taking a thousand signals
13:14
every minute.
13:16
Wind speed, ailerons, control surfaces, flaps, tail position,
13:20
etcetera.
13:21
And it's making
13:22
many, many adjustments because the objective is to
13:25
keep the cabin stable for the passengers
13:27
and not let them get bounced around and
13:29
to keep us on track for where we
13:31
are headed, and that's hard to do manually.
13:33
So what he is implicitly what what he
13:35
was implicitly telling me
13:37
is that from the days of a Cessna
13:39
when the pilot could feel the plane in,
13:41
on the controls
13:43
and manage it,
13:44
to go to a seven forty seven with
13:46
a level of complexity and 400 passengers on
13:48
board, the number of signals exceeded his ability
13:51
to react correctly.
13:53
We have the same problem in health care.
13:55
The number of signals
13:56
has exceeded the ability of anyone to truly
13:59
get it in their head. The ways you
14:01
could arrange a day in an MRI machine
14:03
is a number of more than a 100
14:05
zeros, yet we're expecting people to decide
14:08
in what order patients should be seen. So
14:10
that's why
14:12
we've got a unique opportunity here.
14:14
Very much so. What what year was that
14:16
again that happened? Oh, it was 2000.
14:18
But before 09:11, so a long time ago.
14:21
Now nowadays, there's no chance, on a commercial
14:24
plane, you're getting let into a How relevant
14:26
how relevant twenty five years later to to
14:28
still be thinking about that in terms of
14:30
health care and what systems are trying to
14:32
do and build. Honestly, all of it is
14:35
a bucket list thing to sit in a
14:37
cockpit of a seven forty seven in the
14:38
middle of the night. I I still think
14:40
about that. Very much so.
14:42
I'm gonna add another caveat, if you'll let
14:44
me, Mohan, which is that everything you've described
14:47
thus far, the
14:49
adapting, the learning, the sensing,
14:52
making sense of all of these signals that
14:55
are too overwhelming for any one person.
14:58
This also needs to be connected in the
15:00
health system. You you can't have the ED
15:01
and the OR, the inpatient units, imaging, all
15:04
the other parts going about this with operational
15:07
intelligence if there's still silos. Yeah. So each
15:10
of those individual units can't all go alone
15:12
in this. Where do you see the limits
15:14
of trying to
15:16
do that with the EHR alone when we're
15:18
talking about connecting operational intelligence?
15:21
That's an excellent question, Molly, because here's the
15:24
thing.
15:25
You can think of the hospital as a
15:26
living system where each department is like a
15:29
vital organ. Right? So you've got imaging. You've
15:32
got the ORs. You've got EDs, etcetera. So
15:34
you've got vital organs, and they're all interconnected.
15:37
And they're interconnected because there are so many
15:39
support services.
15:41
Imaging touches everything.
15:43
Patient flow touches everything.
15:45
EVS,
15:47
physiotherapy. Lots of things touch everything. So if
15:49
you thought of it as organs that are
15:51
connected to the circulatory system, then it starts
15:53
to get
15:54
makes sense that they're all interconnected.
15:56
So what happens today?
15:58
Health system leaders in each unit, there are
16:01
two things that are just happening continuously.
16:03
One,
16:04
they're making decisions based on data that was
16:07
either yesterday or right this moment.
16:10
There is very little decision making with precise
16:14
forward oriented predictions.
16:16
Okay. That's that's kind of thing one. Thing
16:18
two,
16:19
many decisions are being made based
16:22
made based on a purview
16:24
of the immediate environment, meaning their local optimization.
16:28
Not saying they're making bad decisions. They're making
16:30
the best decisions
16:32
for their field of vision,
16:34
and that sometimes has adverse consequences
16:37
downstream.
16:39
So the imaging
16:41
registrar who decides to do patient a and
16:44
then patient b because the two
16:47
of them had the same setup on the
16:48
machine, yes, it's more efficient.
16:50
Well but if you've done patient c before
16:53
b, yes, you'd had two setups and you'd
16:55
wasted five minutes,
16:57
but patient c would have got discharged two
16:59
hours earlier
17:00
had you done that.
17:02
That
17:03
but they don't have the visibility to do
17:04
that. So it's not like they're doing something
17:05
wrong. They just don't have the visibility to
17:07
do that.
17:08
EVS
17:09
will optimize the rooms to clean
17:12
based on their crews traveling the least. So
17:14
they will do room one, room two, room
17:16
three
17:17
instead of room one, room 10, room three.
17:20
Well, if 10 needed it faster,
17:22
they don't have the macro view. So they're
17:24
optimizing for what they've got. Everywhere you go
17:26
in health care, there are those two problems,
17:28
which is they're working with current data or
17:30
old data,
17:31
and they're working
17:33
with a local model, not a global model.
17:35
That's why a nervous system that connects it.
17:37
So, you know, when you sense danger to
17:40
one part of your body, your legs get
17:42
into motion to run from it. So so
17:43
you've got to be able to connect everything.
17:46
Now
17:47
the EHR is a fantastic system of record,
17:50
but it was never built to be the
17:51
system of intelligence
17:53
Because firstly, it doesn't have 80% of the
17:55
data you need to do all of this.
17:56
You need stuff from workflows management systems. You
17:59
need data from devices. You need environmental data.
18:01
You just need all kinds of other data.
18:03
So
18:05
you can't expect the HR to solve it
18:07
any more than you could have expected an
18:09
orthopedic surgeon to do cardiac surgery. Brilliant surgeon,
18:11
no doubt, but but it's a different thing.
18:14
And so
18:15
when we think about this scale of technology
18:18
transformations,
18:19
it'll take a thousand companies to do it.
18:21
So when you think about the Internet was
18:23
not delivered by IBM,
18:25
AI will not be delivered by Google or
18:27
Microsoft alone. It's a mosaic of a thousand
18:29
companies,
18:30
and 900 of them have probably not been
18:32
born as yet.
18:34
Mhmm.
18:35
We've been talking so far about the operating
18:38
model being rewired
18:39
by operational
18:41
intelligence and how that just will fire differently.
18:43
But beyond the the model itself, let's talk
18:45
about some of the effects and the results
18:47
that
18:48
systems are trying to achieve through this work.
18:51
Can you talk about, Mohan, if if a
18:53
health system begins to operate more how we're
18:55
describing,
18:56
connected, it's learning,
18:59
what changes should leaders really expect to see,
19:02
whether that's in patient flow,
19:04
staff experience,
19:06
financial performance? What are some results
19:09
most likely here?
19:11
The
19:12
benefit and the beauty of operational excellence is
19:15
it's very tangible, and it's quite
19:18
quick, meaning you don't have to wait years
19:21
to see the results. Right? You can start
19:22
to see it in weeks and months.
19:24
You'll see improvements in flow, and flow will
19:27
manifest as shorter waste wait times, shorter cycle
19:30
times between discharge order and actual discharge, bed
19:35
placement.
19:36
You'll see fewer delays at every step, fewer
19:38
bottlenecks,
19:39
emerging
19:40
and a better utilization of, existing capacity.
19:44
That'll also improve access because the moment you
19:46
get higher velocity and higher movement,
19:49
it enables you to see more patients.
19:52
The shortest
19:53
cycle time to unlock new capacity
19:55
is to move an existing patient onwards because
19:58
that frees up the resource that they were
20:00
just using.
20:01
And as a result, financial performance will improve
20:04
because you're getting more value out of the
20:05
assets you already have.
20:08
Staff spending less time on manual stuff and
20:12
chasing
20:12
and more time with patients will make them
20:15
happier. We'll have less burnout.
20:18
Nobody went to nursing school or medical school
20:21
to become a data entry operator into an
20:23
EHR
20:23
or to become human middleware to patch things
20:26
from here to there and cut and paste
20:27
from one spreadsheet to another. Letting them do
20:30
what inspired them to seek this career in
20:32
the first place
20:33
is very helpful.
20:35
Leaders will spend less time firefighting on day
20:38
to day issues and more time on strategic
20:41
things that they need to. And over time,
20:43
what happens is the system will become more
20:45
predictable, more coordinated, more resilient.
20:47
Think about this. You have probably taken 2,000
20:50
flights in your life, maybe more.
20:53
Many of them were connecting flights. I bet
20:55
you've missed less than five connections in your
20:57
life.
20:58
Right? And think of the uncertainty.
21:01
It's a plane. It's a complex piece of
21:03
equipment flying six miles high at 600 miles
21:05
an hour.
21:06
There's weather. There's cruise. There's delays. There's, you
21:09
know, all kinds of things.
21:12
But they managed to hit 95%
21:15
reliability
21:16
on making you make the connection and making
21:18
your bag make the connection.
21:20
Because the number of times you've lost your
21:21
bag is probably once or twice in your
21:24
lifetime, not not 20 times. Right? And so
21:28
it that is also very helpful for health
21:30
systems.
21:32
The predictive resilient piece and and comparing it
21:34
to flying is is such a great point,
21:37
and that that is what's possible as these
21:39
systems continue to learn
21:40
from their own operations over time. Mohan, you
21:43
mentioned improved access. The financial performance will improve
21:46
staff, less time chart diving,
21:48
chasing
21:49
down things, more time with patients.
21:51
If you step back holistically,
21:54
any other reason
21:55
that this
21:56
matters most for hospital operations to modernize and
22:00
actually get things right at this level
22:03
for for patients and access to care specifically?
22:06
I might be a bit biased, but I
22:08
actually think it's one of the highest value
22:10
things that a health system leader can focus
22:12
on. Because today's
22:14
operating
22:15
model
22:17
shows up as inefficiencies
22:18
in delays,
22:20
whether it's waiting
22:21
for beds, waiting for a scan, waiting for
22:23
treatment. And the consequences
22:26
of the delays are actually hard to quantify.
22:29
If you don't squeeze a patient in for
22:30
an imaging scan
22:32
and the earliest you offer them is four
22:34
weeks out and you lose the scan, it's
22:36
not just the scan. You lost all the
22:37
treatment that followed because where you went the
22:40
scan probably goes the treatment. So the consequences
22:43
are quite high. So improving access allows more
22:46
patients to be treated, which is on point
22:48
and on mission. When operations improve,
22:50
patients move through the system, as we talked
22:52
about, more predictably with less frustration.
22:55
The staff are less frustrated.
22:57
So
22:58
we've talked to a bunch of CFOs. We
23:00
just published a report recently on what CFOs
23:03
said.
23:04
And 72%
23:06
of them, some number of that order, thought
23:08
their operating margins are in the two to
23:09
3% range, which
23:11
is really, really tight margins.
23:14
For running such a complex, high risk,
23:18
high value service
23:20
for
23:21
humanity, 23% is a very tight margin to
23:23
operate on.
23:24
But what that does is it points to
23:26
why this is important.
23:28
Say a health system wanted to improve its
23:29
operating margin by $20,000,000.
23:32
If it tried to do it through just
23:33
regular growth,
23:35
it would need to find a billion dollars
23:37
of net patient revenue for the 2% of
23:39
a billion to translate into 20,000,000.
23:42
Right?
23:43
That's
23:44
a billion of new net patient revenue is
23:46
hard. 20,000,000 in operating improvements by getting sophisticated
23:50
on this stuff is a lot easier, is
23:52
a lot quicker,
23:53
and the ability to deliver it within six
23:56
to nine months,
23:57
we've we've shown it a 100 times all
23:59
over the place.
24:01
Mohan, I I wanna thank you so much
24:02
for this conversation today. It's a lot to
24:04
think about in terms of where health system
24:07
operations have been,
24:08
where you see them going, and ultimately why
24:10
it matters so much. So I wanna thank
24:12
you so much for your expertise, and thank
24:14
you to our listeners for joining us. For
24:17
more conversations like this one, please visit beckershospitalreview.com
24:22
or subscribe wherever you get your podcasts. Mohan,
24:25
thank you again. Molly, thank you.