- Hello everyone, and welcome
to the Becker's Healthcare podcast series.
On today's episode, we're going
to listen in on a recent interview between Scott Becker,
founder and publisher of Becker's Healthcare
and Mohan Gudas, the Chief Executive Officer of Lean Toss,
where they discuss artificial intelligence
and the healthcare workforce.
Thank you all so much for tuning in.
Let's go ahead and get started.
- This is Scott Becker, the publisher
of Becker's Healthcare.
We're joined by the Chief Executive Officer of Lean tos, uh,
to to have a session to discuss making AI work
for the workforce, empowering staff and systems to thrive.
We're joined by Mohan Rados, uh, brilliant,
brilliant leader, uh, Mohan.
I'm gonna ask you for to, to take assessment.
We're gonna talk today about making
AI work for the workforce.
Uh, so much has been discussed about ai.
Before we get started, I'm gonna ask you to take a moment
to, uh, to introduce yourself
and tell us a little bit about yourself,
lean Tass, and your background.
- Terrific. Thanks Scott. It's terrific to be here.
I'm Mohan GDAs. I'm the founder and CEO of Lean Tasks.
Lean Tasks is focused on ai, mathematics, data science,
and software to unlock capacity in health systems.
We currently work with about 180 health systems in the us.
About 800 hospitals across the country use one
or more of our products.
We unlock capacity in three specific areas,
infusion centers, where we power, uh, 12,000 chairs,
roughly 30% of the infusion capacity in the us.
IQ for operating rooms unlocks capacity in ORs.
We power about 5,000 ORs in about 500
hospitals, uh, across the country.
And our third and newest product is inpatient
where we unlock capacity
and improve patient flow and inpatient units.
Uh, we have about 20,000 beds that we currently optimize.
- Thank you. And I've watched the growth
of Lean Toss over the years,
and it's been brilliant to watch this sort of working
to make things work better for systems and,
and fusion centers and, and a lot more.
We're gonna talk today about one
of the biggest challenges facing healthcare.
This, this lingering workforce shortage
and the role that AI powered technology can play
to helping a constrained workforce better operate at the top
of their business, the top of their license
to in a much more efficient, much more efficient way.
So, so Juan, let me get started.
We, we know that workforce challenges are top of mind
for healthcare leaders.
Probably nothing more top of mind, both in terms
of the rising cost of labor, the shortage of clinicians,
and the ability to sort of work through throughput
to make things actually work efficiently
in this challenging time.
The, eh, a's latest, it show
that there's been a 20.8% increase in hospital labor cost.
And so just trying to make sure things work
relatively efficiently.
Uh, we've also seen that that increase
of 21% almost from 2019 to 2022.
There's also, I think the latest numbers
that we saw are 33% more open nursing roles now
than they were three years ago.
Uh, and,
and we also have at least a hundred thousand physician
shortage, 90 to a hundred thousand physician shortage.
And that number with 330 million people in our nation
and only 1,000,070 thousand physicians,
that gap is becoming bigger
and bigger of the sort of clinicians needed, the providers,
the nurses, and the techs and
everybody else to make these work.
And, and let me ask you, you work
with more than 200 health systems, more than 5,000,
6,000 operating rooms.
What are you hearing anecdotally from leaders about
where this is getting worse, better?
The same? What, what are you hearing
from people that you talk to?
- Scott? That's a huge issue.
Staffing shortages have actually been an issue
for quite a while, even before the pandemic.
What the pandemic did was high heighten the severity
of the situation and the urgency for health systems
to make a concerted effort
to get better at optimizing their approach to staffing.
And the reason it's important is it
affects all type of staff.
Provide a burnout is a real thing.
The EHR was supposed to have helped,
but instead, providers are spending more time on EHR screens
than they are talking with patients,
and they have a lot of pajama time,
which is their short form for catching up on
EHR documentation from home after dinner
because they need to do that.
Nursing shortage is also real.
Hospitals are spending much more money on travelers
and on contract nursing than they ever have in the past.
So the way this manifests, uh,
and we see it every day, is hospitals operating well
below their rated capacity.
They're running fewer beds than they're licensed to run,
or they are running fewer operating rooms
than they've actually built.
And so this shortage in capacity
then affects everything else.
And so it's a real problem and, and pretty significant.
- No, thank you. And this is what we hear and see as well.
So thank you for sharing that vantage point.
We toss has products that help sort of work and,
and logistics and drive efficiency and throughput
and help people manage their operating rooms,
their infusion centers, their inpatient bed beds better.
How do your solutions help staff do their jobs better
and reconnect with the joy, the sort of pleasure of actually
practicing or taking care of people versus spending
so much time in the e emr,
so much time on their own screens?
How, how does that help? Uh,
how does this help your workforce?
The workforce,
- If you step back and look at it, optimizing patient flow
through a health system has two pieces to it.
First is optimizing the utilization of the physical asset,
whether it's an operating room, an imaging machine,
an infusion chair, or an inpatient bed.
And then the second piece is optimizing the allocation
of staff associated with that asset in patients.
There are two distinct pieces to it.
Now, the core concept underlying all
of our products is the continuous balance of supply
and demand using sophisticated math
and AI in manufacturing, if there's an imbalance
between supply and demand, you can easily offset it
by just changing the inventory level.
You either manufacture and put it to inventory
or pull it from inventory.
Healthcare has to balance it continuously if they don't,
because you cannot inventory anything.
And if you don't balance it, either a patient waits,
which we see all too often,
or a provider waits, which obviously is frustrating to them,
or an expensive asset just sits idle,
we've all seen $5 million MRI
and CT machines sitting around with no patient in them.
So what our solutions do is first predict the volume
and the type of demand with a stunning level of accuracy.
You have to predict that at a granular
level, highly accurate.
Then you have to understand the supply side,
what the constraints are, facilities, staff,
equipment rooms, et cetera.
And then you figure out the optimal allocation
between supply and demand
to keep patients moving through the system.
When we look at most staffing systems that are out there,
they've essentially grown up over the last 20 years being
time on attendance systems.
That's what they were. So they kept track of
who worked in which unit, how many hours they worked,
was it overtime, was it bonus, double time, et cetera,
and how they should get paid.
This has nothing to do with optimization.
This is a administrative payroll kind of a function.
And so what we find is when you do the optimization,
the way we are talking about, you can then
construct shift schedules and rosters to meet the demand.
And then you start making intelligent recommendations.
Now, if you think about what burns people out
and what, what causes frustration,
frontline staff every day
are highly skilled people doing menial tasks.
They're pulling numbers, they're sending text messages,
they're leaving voicemails, they're chasing people.
All of this could have been automated.
And so we help, we believe
that if we help staff anticipate what's coming
through prediction, reduce their cognitive overload
by making intelligent recommendations so they don't have
to think as hard, uh,
and then you automate the routine tasks,
you will free up their mind space,
which then they can go back to doing what they love,
which is taking care of patients.
And so that is what we believe is the core.
- So, so in the ideal situation, a much, much greater fit
of supply and demand and,
and a lot of taking the logistics, at least the, the out
of the, the paper, the logistics, the guessing logistics
and so forth so that nurses, physicians,
clinicians can spend more time just taking care
of patients versus worrying
so much about is black time being used as it not being used
as this person showing up and they're not showing up?
How are we turning over operating
rooms and, and everything else?
Talk to us now about how AI is gonna sort of help
to revolutionize this.
There, there's lots of concerns from people.
Is AI gonna take away jobs?
Is it going to replace human jobs or, or, or,
or get, get rid of roles.
I mean, we've seen some of this automation in some places
where, you know, you go to your bank
and there used to be a teller and now it's an ATM,
and of course we've all going to love that,
but people are still concerned about is this gonna hurt jobs
and is this gonna take away jobs?
How do you get buy-in from overburdened frontline staff to
to, to try and implement some of this technology
when some technology that they work with
creates more harm than help?
You talked about the EMR, and
obviously we're not living without the EMRs.
EMRs have been extremely helpful,
but they have added a level of burden
that people didn't expect and exactly.
You talked about people coming home,
you talked about pajama time, people being at
home, working in those things.
What mindset shift has to occur for staff to buy into,
to feel like AI is helping versus adding more
burdens or taking away jobs?
How do you sort of get positive clinician buy-in
to new technology and AI driven technology?
- The first thing is, it's important to be realistic
and not over promise what AI can do for the frontline.
The way we think about it is it's augmented intelligence.
It's not artificial intelligence
that replaces the human being.
It just gives them a set of recommendations
that are intelligent and that they can think about
and perhaps consider making.
So in our view, the frontline has,
has the final mile of decision making.
So we prescribe intelligent recommendations,
but we expect that the frontline has got clinical context,
patient context, provider context,
that they should make the final override
or go no-go decision on it.
The black box should not make the decision for them.
So the way we think about it is,
imagine the navigation system in your car.
It's constantly suggesting an intelligent route to get
to your destination faster,
but you as the driver can always override it based
on your unique knowledge.
If it's late at night on the freeway in a not so great part
of town, and it tells you, Hey, get off the freeway
and you can say five minutes, you might say, yeah,
no, I don't want to get off the freeway.
Right? So you've applied the last minute judgment
that overrides it.
What does the navigation system do?
It reacts to that and says, fine,
and it suggests the next best route.
And you can constantly override it
until you think it's got a good idea, uh, and you follow it.
So what happens over time is the frontline gains confidence
that the recommendations are actually intelligent
and they start relying on it more and more.
And it starts to feel natural, just like we all do
with Netflix recommendations when it recommends movies
to us, it turns out it's pretty accurate.
Or Amazon recommends things for us to buy
or on navigation systems, as I said, right?
So the same clinicians
and providers who are unwilling
to accept an AI recommendation at the end of the day,
get into the their cars and drive home
and happily accept all the navigation
system recommendations.
And so we just have to, uh, make sure we do that.
The other thing we have to do is not pretend
to have expertise we don't have.
So when we make operational recommendations, we don't try
and make clinical recommendations where we are trying
to out dock the docs where we pretend
to have clinical judgment
that exceeds theirs because we don't.
So we are very clear that we've got operational expertise,
we'll make excellent operational recommendations
and leave the clinical judgment to the people who know best.
- Thank you. It's a, it's a fascinating
and brilliant perspective as to how to look at this
and compare it to the sort of navigation system,
Netflix suggestions, the Amazon suggestions, right?
We get that, the choices and ideas,
and then we decide, are we
doing this or we're not doing that.
Exactly. And I think that's a,
a brilliant way to look at it.
And all those things, as you know, have gotten
so good at making those suggestions.
So they're easy to make a choice.
I'm gonna continue on my route, I'm gonna take the
suggestion, but there's not so much
internal fighting over trying to figure out what am I doing?
We sort of react to it very naturally
from other applications, previous AI
and analytics implementation line, what, you know, in, in
regarding what have you learned
or what have we all learned in healthcare from previous
efforts in AI
and analytics implementations in healthcare
regarding things like stakeholder engagement.
How, how much will the stakeholder actually use it?
We all, we all are familiar with,
you guys have done this brilliant job of making the software
and, and, and wing toss on the system
that people actually use,
but we're all familiar with lots
of systems people have bought
and they use by 1% of the workforce,
or you only use, you know, 10%
of the applicability and so forth.
What have we learned about stakeholder engagement culture
and change management that could be applied now
to health systems to make them better users of AI
and better users of systems?
- I think the first thing is that senior leadership has
to buy in into one simple fact.
The operational complexity of healthcare has gone up by 10 x
or a hundred x in the last 10 or 15 years,
but the operational practices have not kept up with
that growth of complexity.
They still pull dashboards from EHRs.
They manually do the best they can.
This is just not gonna be good enough on a go forward basis.
So senior leadership has to accept that concept.
Then they have to be committed to exploring the art
of the possible to just start to understand the level
of sophistication that's inside things.
We take for granted the yield management
and airlines, how they fill planes
or other com operationally complex environments
and how it might be applied to healthcare.
Could you just step back for a moment
and imagine what would happen if airlines assign seats
to passengers the way health systems assign appointments
to patients, right?
Or if airlines allocated pilots
and aircraft to routes the way health system assign ORs
to surgeons or service lines, that would just,
it would just be permanently gridlocked.
And we can see what happens once in a while.
There's a big storm or a software outage
that throws airline operations outta whack for two days,
and it's the news, it's everywhere.
The reality is health systems are operating in
that state pretty much every day.
They're at the edge of capacity, uh,
and they're making operational decisions manually.
So they are almost operating in that manner every day.
So the first thing is you need change the senior leadership
to buy into the complexity and it takes different tools
and then the mindset to explore the art of the possible.
Second is everybody talks about change management
and it's very important, but here's the interesting thing.
The frontline wants to do the right thing.
They want to take care of patients, they want
to do it in a safe and effective manner.
So you know how to fight the frontline
to do something they don't want to do.
That's when change management
and you're trying to persuade someone
to do something they don't want to do.
They want to do this, they just need better tools to do it.
So if you can persuade them that what we are providing
is easy, it's intuitive,
and they can do the right thing all the time,
it soon becomes in their enlightened self-interest
to use the tools, you're now not trying to fool them
or persuade them or cajole them or threaten them to use it.
They just find it's easier
to do the right thing quickly all the time.
Think about it, nobody from Uber came to my house
to apply change management psychology to me, to convince me
to start using the Uber app.
It is just the simply the best way to go from point A
to point B at short notice.
I therefore use it. There was no Uber change management, uh,
school that they had to run for me.
So that's what we aspire to do with our tools.
Make it easy and intuitive
to do the right thing all the time.
- No, and it's, and it's so critical that ease of usability
so people can, can start to plug in
and use it without feeling like there's incredible learning
curve or incredible challenges to it.
To, to, to making it useful
and not having to go through an incredible amount
of change management, but actually easy to use.
You start to adopt it, you start to use it and,
and you love it and it makes sense to do the right thing.
I I love that perspective.
One of things that's being talked about right now, just to,
to take it to one further
diving into the workforce issue on this,
is there's a tremendous amount of clinician burnout.
You know, different articles talk about, there's a,
a recent article journal of general internal medicine report
that shows 50% of all healthcare professionals are,
are burnt out some way
or another are, are at least facing burnout.
How can technology positively impacts staff burnout versus
being another burden on top of, of people
or just sort of operationally,
but aside from sort of improving operational metrics,
helping us deal with supply
and demand, how can it actually help sort
of clinician burnout and,
and positively impact the human situation?
- Let's think about all the things that cause stress.
Operating at the edge
of capacity every single day is exhausting.
Imagine if you had to drive in rush hour traffic nonstop
for eight to 10 hours every single day, right?
We get away with rush hour traffic
because an hour in each direction and we call it a day.
But the, the frontline staff is living in rush hour
conditions where everything is a crisis
and everything comes at them.
So that's one source of, uh, exhaustion that comes in.
Spending a large chunk of your day doing routine
mundane tasks that are way beyond your skilled
licensed level is frustrating.
And yet we force our frontline
to do a lot of mundane things.
And here they are highly qualified nurses and caregivers
and we are forcing them to pull data from spreadsheets
and cut and paste into things.
Having the staff feel overwhelmed all the time where
they're reacting to issues one at a time, somehow getting
through it only to have the next issue and the next issue
and the next issue come at, come at them like a tsunami.
They somehow power through it, get through the day,
and then like groundhog data will happen again tomorrow.
That is stressful. Imagine if they could anticipate the
issues, prepare for it in advance
and not have to deal with it, right?
Just think about going through life.
If everything was a surprise to you,
life would be a lot more stressful than if you could plan
what's happening today, tomorrow, next week, next month.
And then finally, they are forced to make decisions
that are not just minor decisions.
They impact a patient's health, maybe their life,
and they don't have all the information at their fingertips.
They don't have access to everything they need.
And that creates anxiety that I am,
I sure I'm doing the right thing.
And, and so we've got many things
that create the perfect storm from the manual tasks
to the being overwhelmed to, you know,
not having all the information at your fingertips
that causes the, the stress.
And we think that our approach to it can, uh, uh, you know,
can solve many of the problems.
Obviously we can't solve every problem in front of them,
but we can do a lot to alleviate,
alleviate the stress in the burnout.
- But, but your point on this concept
of living in a relatively constant fire trail,
and it's not always the case, but it's,
but it's a lot the case reminds you of
where when travel goes wrong
and how stressed out the staff
is 'cause travel has gone wrong.
'cause they're dealing with every single person trying
to change and figure out what flight to get on,
and they don't necessarily have all the logistics to do it.
Well, is is the situation you're talking about,
that stress level is very constant in,
in large health systems.
And so your point on how can we make that more planned,
more, more systematic, a a little bit less stress,
a little bit less fire drills goes a long way in terms
of making just the environment easier
to work in? , you're
- Exactly right, Scott.
They, they call it, uh, airlines, call it iop,
irregular operations.
So when, uh, there's a storm in Chicago
and 2000 people have missed their connecting flights
and you need to rebook them,
there's a whole different method that comes into play
where the airline is in IOP mode,
where they're in irregular operations
and their goal is to get out of IO into regular operations
as quickly as they can.
And your point's exactly right,
health systems are often operating always in IOP mode,
and that's what, uh, is stressful.
- That sounds exactly right.
You and a colleague,
co-authored the book on hospital operations,
a brilliant book on Better Healthcare
through math is I think the title of, I've got that right.
Is it, is that right? That's right.
In in the year ahead,
what do you anticipate will be the biggest,
the single biggest challenge that
that hospital executive teams and leadership teams face?
And, and, and how can leaders stay ahead of that now?
What, what are the biggest challenges
or the biggest challenge that
operations teams are gonna face?
- Yeah, so staying on the operational side,
because obviously clinical, there's a whole bunch of things.
We are not expert on the clinical,
but if I think about the single biggest challenge from an
operational perspective for health system leaders is
to do more with the existing assets
and the existing staff that are already in place now, right?
In the past, health systems could simply build their way
out of operational problems.
When they ran into operational problems,
they constructed more hospitals, put in more ORs,
put in more inpatient units,
built ambulatory facilities, they kept building.
Now they are under enormous pressure from many directions.
Demand is going up. We've got a aging population,
more chronic illnesses, reimbursement levels are down,
input costs, particularly labor are rising like crazy.
So that makes it, uh, that makes it particularly hard.
And the result of all
of these forces is the financial performance
of most health systems has deteriorated right now.
Therefore, they have to do more with less.
If you look at asset utilization broadly
measured in healthcare,
it lags every other asset intensive service
business by a lot.
Now, some of it's not controllable.
Other service businesses can operate
24 hours around the clock.
Healthcare essentially is at best a 12 hour operation.
It's a 7:00 AM to 7:00 PM kind of operation.
So by definition, if you are perfect,
you can be 50% utilized.
So that's, uh, you know, a starting point that's,
that's difficult, but they can do much better.
And if you looked at what is the value at stake,
there are 5,000 hospitals in this country.
On average, they have three
or $400 million in assets, not counting real estate.
So an or is 10
or $15 million, an imaging machine is three or $4 million.
Inpatient units are expensive.
Three or $400 million per hospital across 5,000 hospitals
is $2 trillion of assets.
If you unlock 10 points of productivity,
you can pump $200 billion of value
per year into this country's healthcare system, right?
That means it's 30 or 40 million per hospital, which is
straight bottom line effects.
And if you did that, it's a boost to the bottom line.
But more importantly, it has the ability
to serve patients more effectively, more access,
better service, faster throughput, less wait time.
And so the value at stake is enormous.
And that's kind of our sole focus on what we're trying
to deliver to our customers every day.
- No, thank you for that.
I mean, uh, unlocking that value in those assets
that are already there versus building more assets,
you know, and, and, and tying it back
to the patient, into the workforce.
This critical concept of reducing burnout
by making life
and work a little bit more manageable by,
by having better predictability, better analytics,
better recommendations on how to, how to manage operations
or, or, or, or better tools is so important.
Mohan, as always, I want to thank you
for taking the time to visit with you.
To me, I always learn a lot when I speak to you
and your leadership about sort of hospital operations and,
and how, you know, the supply demand,
the logistics can be better managed and,
and how you're doing that at 5,000 plus operating rooms,
hundreds and hundreds of infusion
centers and, and a lot more.
Thank you for joining us on this discussion, sort
of AI in the workforce, empowering staff
and systems to thrive.
So, so important to, to make things more predictable,
getting outta what you call this IROC concept,
this a regular operations concept of a constant fire drill,
which causes so much, so much stress and so much burnout.
Moan, thank you very much for, for visiting. Thank you,
- God, great to chat with you as always.
Thank you.
- Well, thanks again to our listeners for tuning in today.
I'd also like to thank Lean Toss
for sponsoring this episode.
You can tune into more podcasts from Becker's Healthcare
by visiting our podcast page at becker's podcast.com.