Latest / Becker’s Healthcare Digital Health + Health IT / Empowering Staff to Thrive: Tackling Burnout with AI in Healthcare Operations - Sponsored by LeanTaaS

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Empowering Staff to Thrive: Tackling Burnout with AI in Healthcare Operations - Sponsored by LeanTaaS · Becker’s Healthcare Digital Health + Health IT

• 0:00
  - Hello everyone, and welcome
• 0:01
  to the Becker's Healthcare podcast series.
• 0:03
  On today's episode, we're going
• 0:05
  to listen in on a recent interview between Scott Becker,
• 0:08
  founder and publisher of Becker's Healthcare
• 0:10
  and Mohan Gudas, the Chief Executive Officer of Lean Toss,
• 0:14
  where they discuss artificial intelligence
• 0:16
  and the healthcare workforce.
• 0:18
  Thank you all so much for tuning in.
• 0:20
  Let's go ahead and get started.
• 0:22
  - This is Scott Becker, the publisher
• 0:24
  of Becker's Healthcare.
• 0:26
  We're joined by the Chief Executive Officer of Lean tos, uh,
• 0:30
  to to have a session to discuss making AI work
• 0:34
  for the workforce, empowering staff and systems to thrive.
• 0:38
  We're joined by Mohan Rados, uh, brilliant,
• 0:42
  brilliant leader, uh, Mohan.
• 0:44
  I'm gonna ask you for to, to take assessment.
• 0:46
  We're gonna talk today about making
• 0:47
  AI work for the workforce.
• 0:49
  Uh, so much has been discussed about ai.
• 0:52
  Before we get started, I'm gonna ask you to take a moment
• 0:54
  to, uh, to introduce yourself
• 0:56
  and tell us a little bit about yourself,
• 0:58
  lean Tass, and your background.
• 1:00
  - Terrific. Thanks Scott. It's terrific to be here.
• 1:02
  I'm Mohan GDAs. I'm the founder and CEO of Lean Tasks.
• 1:06
  Lean Tasks is focused on ai, mathematics, data science,
• 1:11
  and software to unlock capacity in health systems.
• 1:15
  We currently work with about 180 health systems in the us.
• 1:18
  About 800 hospitals across the country use one
• 1:22
  or more of our products.
• 1:24
  We unlock capacity in three specific areas,
• 1:27
  infusion centers, where we power, uh, 12,000 chairs,
• 1:31
  roughly 30% of the infusion capacity in the us.
• 1:35
  IQ for operating rooms unlocks capacity in ORs.
• 1:39
  We power about 5,000 ORs in about 500
• 1:43
  hospitals, uh, across the country.
• 1:45
  And our third and newest product is inpatient
• 1:48
  where we unlock capacity
• 1:49
  and improve patient flow and inpatient units.
• 1:52
  Uh, we have about 20,000 beds that we currently optimize.
• 1:58
  - Thank you. And I've watched the growth
• 2:00
  of Lean Toss over the years,
• 2:01
  and it's been brilliant to watch this sort of working
• 2:04
  to make things work better for systems and,
• 2:07
  and fusion centers and, and a lot more.
• 2:10
  We're gonna talk today about one
• 2:12
  of the biggest challenges facing healthcare.
• 2:14
  This, this lingering workforce shortage
• 2:16
  and the role that AI powered technology can play
• 2:20
  to helping a constrained workforce better operate at the top
• 2:24
  of their business, the top of their license
• 2:25
  to in a much more efficient, much more efficient way.
• 2:28
  So, so Juan, let me get started.
• 2:31
  We, we know that workforce challenges are top of mind
• 2:33
  for healthcare leaders.
• 2:35
  Probably nothing more top of mind, both in terms
• 2:37
  of the rising cost of labor, the shortage of clinicians,
• 2:41
  and the ability to sort of work through throughput
• 2:43
  to make things actually work efficiently
• 2:45
  in this challenging time.
• 2:47
  The, eh, a's latest, it show
• 2:50
  that there's been a 20.8% increase in hospital labor cost.
• 2:54
  And so just trying to make sure things work
• 2:56
  relatively efficiently.
• 2:58
  Uh, we've also seen that that increase
• 3:00
  of 21% almost from 2019 to 2022.
• 3:05
  There's also, I think the latest numbers
• 3:07
  that we saw are 33% more open nursing roles now
• 3:12
  than they were three years ago.
• 3:13
  Uh, and,
• 3:15
  and we also have at least a hundred thousand physician
• 3:18
  shortage, 90 to a hundred thousand physician shortage.
• 3:21
  And that number with 330 million people in our nation
• 3:24
  and only 1,000,070 thousand physicians,
• 3:26
  that gap is becoming bigger
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  and bigger of the sort of clinicians needed, the providers,
• 3:31
  the nurses, and the techs and
• 3:32
  everybody else to make these work.
• 3:34
  And, and let me ask you, you work
• 3:35
  with more than 200 health systems, more than 5,000,
• 3:38
  6,000 operating rooms.
• 3:40
  What are you hearing anecdotally from leaders about
• 3:43
  where this is getting worse, better?
• 3:45
  The same? What, what are you hearing
• 3:46
  from people that you talk to?
• 3:48
  - Scott? That's a huge issue.
• 3:49
  Staffing shortages have actually been an issue
• 3:52
  for quite a while, even before the pandemic.
• 3:54
  What the pandemic did was high heighten the severity
• 3:57
  of the situation and the urgency for health systems
• 4:01
  to make a concerted effort
• 4:03
  to get better at optimizing their approach to staffing.
• 4:07
  And the reason it's important is it
• 4:09
  affects all type of staff.
• 4:11
  Provide a burnout is a real thing.
• 4:14
  The EHR was supposed to have helped,
• 4:16
  but instead, providers are spending more time on EHR screens
• 4:20
  than they are talking with patients,
• 4:22
  and they have a lot of pajama time,
• 4:24
  which is their short form for catching up on
• 4:27
  EHR documentation from home after dinner
• 4:30
  because they need to do that.
• 4:32
  Nursing shortage is also real.
• 4:34
  Hospitals are spending much more money on travelers
• 4:37
  and on contract nursing than they ever have in the past.
• 4:41
  So the way this manifests, uh,
• 4:43
  and we see it every day, is hospitals operating well
• 4:48
  below their rated capacity.
• 4:50
  They're running fewer beds than they're licensed to run,
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  or they are running fewer operating rooms
• 4:55
  than they've actually built.
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  And so this shortage in capacity
• 5:00
  then affects everything else.
• 5:01
  And so it's a real problem and, and pretty significant.
• 5:05
  - No, thank you. And this is what we hear and see as well.
• 5:07
  So thank you for sharing that vantage point.
• 5:11
  We toss has products that help sort of work and,
• 5:14
  and logistics and drive efficiency and throughput
• 5:17
  and help people manage their operating rooms,
• 5:20
  their infusion centers, their inpatient bed beds better.
• 5:23
  How do your solutions help staff do their jobs better
• 5:26
  and reconnect with the joy, the sort of pleasure of actually
• 5:29
  practicing or taking care of people versus spending
• 5:32
  so much time in the e emr,
• 5:33
  so much time on their own screens?
• 5:35
  How, how does that help? Uh,
• 5:36
  how does this help your workforce?
• 5:37
  The workforce,
• 5:39
  - If you step back and look at it, optimizing patient flow
• 5:42
  through a health system has two pieces to it.
• 5:45
  First is optimizing the utilization of the physical asset,
• 5:49
  whether it's an operating room, an imaging machine,
• 5:51
  an infusion chair, or an inpatient bed.
• 5:53
  And then the second piece is optimizing the allocation
• 5:57
  of staff associated with that asset in patients.
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  There are two distinct pieces to it.
• 6:02
  Now, the core concept underlying all
• 6:06
  of our products is the continuous balance of supply
• 6:09
  and demand using sophisticated math
• 6:12
  and AI in manufacturing, if there's an imbalance
• 6:15
  between supply and demand, you can easily offset it
• 6:18
  by just changing the inventory level.
• 6:20
  You either manufacture and put it to inventory
• 6:23
  or pull it from inventory.
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  Healthcare has to balance it continuously if they don't,
• 6:28
  because you cannot inventory anything.
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  And if you don't balance it, either a patient waits,
• 6:33
  which we see all too often,
• 6:35
  or a provider waits, which obviously is frustrating to them,
• 6:39
  or an expensive asset just sits idle,
• 6:41
  we've all seen $5 million MRI
• 6:43
  and CT machines sitting around with no patient in them.
• 6:47
  So what our solutions do is first predict the volume
• 6:51
  and the type of demand with a stunning level of accuracy.
• 6:55
  You have to predict that at a granular
• 6:57
  level, highly accurate.
• 6:59
  Then you have to understand the supply side,
• 7:00
  what the constraints are, facilities, staff,
• 7:03
  equipment rooms, et cetera.
• 7:05
  And then you figure out the optimal allocation
• 7:07
  between supply and demand
• 7:08
  to keep patients moving through the system.
• 7:11
  When we look at most staffing systems that are out there,
• 7:15
  they've essentially grown up over the last 20 years being
• 7:18
  time on attendance systems.
• 7:19
  That's what they were. So they kept track of
• 7:22
  who worked in which unit, how many hours they worked,
• 7:24
  was it overtime, was it bonus, double time, et cetera,
• 7:27
  and how they should get paid.
• 7:29
  This has nothing to do with optimization.
• 7:31
  This is a administrative payroll kind of a function.
• 7:34
  And so what we find is when you do the optimization,
• 7:38
  the way we are talking about, you can then
• 7:41
  construct shift schedules and rosters to meet the demand.
• 7:44
  And then you start making intelligent recommendations.
• 7:48
  Now, if you think about what burns people out
• 7:51
  and what, what causes frustration,
• 7:54
  frontline staff every day
• 7:56
  are highly skilled people doing menial tasks.
• 7:59
  They're pulling numbers, they're sending text messages,
• 8:02
  they're leaving voicemails, they're chasing people.
• 8:05
  All of this could have been automated.
• 8:07
  And so we help, we believe
• 8:09
  that if we help staff anticipate what's coming
• 8:12
  through prediction, reduce their cognitive overload
• 8:15
  by making intelligent recommendations so they don't have
• 8:18
  to think as hard, uh,
• 8:20
  and then you automate the routine tasks,
• 8:23
  you will free up their mind space,
• 8:25
  which then they can go back to doing what they love,
• 8:28
  which is taking care of patients.
• 8:30
  And so that is what we believe is the core.
• 8:33
  - So, so in the ideal situation, a much, much greater fit
• 8:37
  of supply and demand and,
• 8:39
  and a lot of taking the logistics, at least the, the out
• 8:43
  of the, the paper, the logistics, the guessing logistics
• 8:46
  and so forth so that nurses, physicians,
• 8:48
  clinicians can spend more time just taking care
• 8:50
  of patients versus worrying
• 8:52
  so much about is black time being used as it not being used
• 8:54
  as this person showing up and they're not showing up?
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  How are we turning over operating
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  rooms and, and everything else?
• 8:59
  Talk to us now about how AI is gonna sort of help
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  to revolutionize this.
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  There, there's lots of concerns from people.
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  Is AI gonna take away jobs?
• 9:10
  Is it going to replace human jobs or, or, or,
• 9:14
  or get, get rid of roles.
• 9:17
  I mean, we've seen some of this automation in some places
• 9:19
  where, you know, you go to your bank
• 9:21
  and there used to be a teller and now it's an ATM,
• 9:23
  and of course we've all going to love that,
• 9:25
  but people are still concerned about is this gonna hurt jobs
• 9:28
  and is this gonna take away jobs?
• 9:30
  How do you get buy-in from overburdened frontline staff to
• 9:34
  to, to try and implement some of this technology
• 9:39
  when some technology that they work with
• 9:41
  creates more harm than help?
• 9:43
  You talked about the EMR, and
• 9:44
  obviously we're not living without the EMRs.
• 9:45
  EMRs have been extremely helpful,
• 9:47
  but they have added a level of burden
• 9:50
  that people didn't expect and exactly.
• 9:52
  You talked about people coming home,
• 9:53
  you talked about pajama time, people being at
• 9:54
  home, working in those things.
• 9:56
  What mindset shift has to occur for staff to buy into,
• 10:00
  to feel like AI is helping versus adding more
• 10:02
  burdens or taking away jobs?
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  How do you sort of get positive clinician buy-in
• 10:08
  to new technology and AI driven technology?
• 10:12
  - The first thing is, it's important to be realistic
• 10:14
  and not over promise what AI can do for the frontline.
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  The way we think about it is it's augmented intelligence.
• 10:22
  It's not artificial intelligence
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  that replaces the human being.
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  It just gives them a set of recommendations
• 10:28
  that are intelligent and that they can think about
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  and perhaps consider making.
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  So in our view, the frontline has,
• 10:38
  has the final mile of decision making.
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  So we prescribe intelligent recommendations,
• 10:43
  but we expect that the frontline has got clinical context,
• 10:47
  patient context, provider context,
• 10:49
  that they should make the final override
• 10:51
  or go no-go decision on it.
• 10:53
  The black box should not make the decision for them.
• 10:56
  So the way we think about it is,
• 10:57
  imagine the navigation system in your car.
• 11:00
  It's constantly suggesting an intelligent route to get
• 11:04
  to your destination faster,
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  but you as the driver can always override it based
• 11:09
  on your unique knowledge.
• 11:11
  If it's late at night on the freeway in a not so great part
• 11:15
  of town, and it tells you, Hey, get off the freeway
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  and you can say five minutes, you might say, yeah,
• 11:20
  no, I don't want to get off the freeway.
• 11:21
  Right? So you've applied the last minute judgment
• 11:24
  that overrides it.
• 11:26
  What does the navigation system do?
• 11:27
  It reacts to that and says, fine,
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  and it suggests the next best route.
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  And you can constantly override it
• 11:33
  until you think it's got a good idea, uh, and you follow it.
• 11:37
  So what happens over time is the frontline gains confidence
• 11:40
  that the recommendations are actually intelligent
• 11:43
  and they start relying on it more and more.
• 11:45
  And it starts to feel natural, just like we all do
• 11:48
  with Netflix recommendations when it recommends movies
• 11:51
  to us, it turns out it's pretty accurate.
• 11:53
  Or Amazon recommends things for us to buy
• 11:56
  or on navigation systems, as I said, right?
• 11:58
  So the same clinicians
• 12:00
  and providers who are unwilling
• 12:03
  to accept an AI recommendation at the end of the day,
• 12:06
  get into the their cars and drive home
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  and happily accept all the navigation
• 12:10
  system recommendations.
• 12:11
  And so we just have to, uh, make sure we do that.
• 12:14
  The other thing we have to do is not pretend
• 12:17
  to have expertise we don't have.
• 12:19
  So when we make operational recommendations, we don't try
• 12:22
  and make clinical recommendations where we are trying
• 12:25
  to out dock the docs where we pretend
• 12:27
  to have clinical judgment
• 12:29
  that exceeds theirs because we don't.
• 12:31
  So we are very clear that we've got operational expertise,
• 12:34
  we'll make excellent operational recommendations
• 12:37
  and leave the clinical judgment to the people who know best.
• 12:41
  - Thank you. It's a, it's a fascinating
• 12:43
  and brilliant perspective as to how to look at this
• 12:45
  and compare it to the sort of navigation system,
• 12:46
  Netflix suggestions, the Amazon suggestions, right?
• 12:49
  We get that, the choices and ideas,
• 12:51
  and then we decide, are we
• 12:52
  doing this or we're not doing that.
• 12:53
  Exactly. And I think that's a,
• 12:54
  a brilliant way to look at it.
• 12:55
  And all those things, as you know, have gotten
• 12:57
  so good at making those suggestions.
• 13:00
  So they're easy to make a choice.
• 13:01
  I'm gonna continue on my route, I'm gonna take the
• 13:03
  suggestion, but there's not so much
• 13:05
  internal fighting over trying to figure out what am I doing?
• 13:07
  We sort of react to it very naturally
• 13:11
  from other applications, previous AI
• 13:14
  and analytics implementation line, what, you know, in, in
• 13:18
  regarding what have you learned
• 13:21
  or what have we all learned in healthcare from previous
• 13:23
  efforts in AI
• 13:25
  and analytics implementations in healthcare
• 13:27
  regarding things like stakeholder engagement.
• 13:29
  How, how much will the stakeholder actually use it?
• 13:31
  We all, we all are familiar with,
• 13:33
  you guys have done this brilliant job of making the software
• 13:35
  and, and, and wing toss on the system
• 13:37
  that people actually use,
• 13:38
  but we're all familiar with lots
• 13:39
  of systems people have bought
• 13:41
  and they use by 1% of the workforce,
• 13:42
  or you only use, you know, 10%
• 13:45
  of the applicability and so forth.
• 13:47
  What have we learned about stakeholder engagement culture
• 13:49
  and change management that could be applied now
• 13:52
  to health systems to make them better users of AI
• 13:55
  and better users of systems?
• 13:58
  - I think the first thing is that senior leadership has
• 14:00
  to buy in into one simple fact.
• 14:04
  The operational complexity of healthcare has gone up by 10 x
• 14:09
  or a hundred x in the last 10 or 15 years,
• 14:12
  but the operational practices have not kept up with
• 14:16
  that growth of complexity.
• 14:17
  They still pull dashboards from EHRs.
• 14:19
  They manually do the best they can.
• 14:22
  This is just not gonna be good enough on a go forward basis.
• 14:25
  So senior leadership has to accept that concept.
• 14:28
  Then they have to be committed to exploring the art
• 14:32
  of the possible to just start to understand the level
• 14:35
  of sophistication that's inside things.
• 14:38
  We take for granted the yield management
• 14:40
  and airlines, how they fill planes
• 14:42
  or other com operationally complex environments
• 14:45
  and how it might be applied to healthcare.
• 14:47
  Could you just step back for a moment
• 14:49
  and imagine what would happen if airlines assign seats
• 14:53
  to passengers the way health systems assign appointments
• 14:56
  to patients, right?
• 14:58
  Or if airlines allocated pilots
• 15:01
  and aircraft to routes the way health system assign ORs
• 15:06
  to surgeons or service lines, that would just,
• 15:08
  it would just be permanently gridlocked.
• 15:11
  And we can see what happens once in a while.
• 15:13
  There's a big storm or a software outage
• 15:16
  that throws airline operations outta whack for two days,
• 15:19
  and it's the news, it's everywhere.
• 15:22
  The reality is health systems are operating in
• 15:24
  that state pretty much every day.
• 15:25
  They're at the edge of capacity, uh,
• 15:28
  and they're making operational decisions manually.
• 15:30
  So they are almost operating in that manner every day.
• 15:33
  So the first thing is you need change the senior leadership
• 15:36
  to buy into the complexity and it takes different tools
• 15:41
  and then the mindset to explore the art of the possible.
• 15:43
  Second is everybody talks about change management
• 15:47
  and it's very important, but here's the interesting thing.
• 15:50
  The frontline wants to do the right thing.
• 15:53
  They want to take care of patients, they want
• 15:55
  to do it in a safe and effective manner.
• 15:57
  So you know how to fight the frontline
• 15:59
  to do something they don't want to do.
• 16:01
  That's when change management
• 16:02
  and you're trying to persuade someone
• 16:04
  to do something they don't want to do.
• 16:06
  They want to do this, they just need better tools to do it.
• 16:10
  So if you can persuade them that what we are providing
• 16:14
  is easy, it's intuitive,
• 16:17
  and they can do the right thing all the time,
• 16:19
  it soon becomes in their enlightened self-interest
• 16:21
  to use the tools, you're now not trying to fool them
• 16:24
  or persuade them or cajole them or threaten them to use it.
• 16:27
  They just find it's easier
• 16:29
  to do the right thing quickly all the time.
• 16:32
  Think about it, nobody from Uber came to my house
• 16:35
  to apply change management psychology to me, to convince me
• 16:38
  to start using the Uber app.
• 16:40
  It is just the simply the best way to go from point A
• 16:43
  to point B at short notice.
• 16:45
  I therefore use it. There was no Uber change management, uh,
• 16:48
  school that they had to run for me.
• 16:50
  So that's what we aspire to do with our tools.
• 16:53
  Make it easy and intuitive
• 16:54
  to do the right thing all the time.
• 16:57
  - No, and it's, and it's so critical that ease of usability
• 17:01
  so people can, can start to plug in
• 17:03
  and use it without feeling like there's incredible learning
• 17:06
  curve or incredible challenges to it.
• 17:09
  To, to, to making it useful
• 17:11
  and not having to go through an incredible amount
• 17:13
  of change management, but actually easy to use.
• 17:16
  You start to adopt it, you start to use it and,
• 17:18
  and you love it and it makes sense to do the right thing.
• 17:21
  I I love that perspective.
• 17:23
  One of things that's being talked about right now, just to,
• 17:26
  to take it to one further
• 17:28
  diving into the workforce issue on this,
• 17:30
  is there's a tremendous amount of clinician burnout.
• 17:34
  You know, different articles talk about, there's a,
• 17:36
  a recent article journal of general internal medicine report
• 17:40
  that shows 50% of all healthcare professionals are,
• 17:43
  are burnt out some way
• 17:44
  or another are, are at least facing burnout.
• 17:47
  How can technology positively impacts staff burnout versus
• 17:51
  being another burden on top of, of people
• 17:53
  or just sort of operationally,
• 17:56
  but aside from sort of improving operational metrics,
• 17:58
  helping us deal with supply
• 18:00
  and demand, how can it actually help sort
• 18:02
  of clinician burnout and,
• 18:04
  and positively impact the human situation?
• 18:08
  - Let's think about all the things that cause stress.
• 18:12
  Operating at the edge
• 18:13
  of capacity every single day is exhausting.
• 18:17
  Imagine if you had to drive in rush hour traffic nonstop
• 18:20
  for eight to 10 hours every single day, right?
• 18:23
  We get away with rush hour traffic
• 18:24
  because an hour in each direction and we call it a day.
• 18:27
  But the, the frontline staff is living in rush hour
• 18:30
  conditions where everything is a crisis
• 18:33
  and everything comes at them.
• 18:34
  So that's one source of, uh, exhaustion that comes in.
• 18:39
  Spending a large chunk of your day doing routine
• 18:42
  mundane tasks that are way beyond your skilled
• 18:44
  licensed level is frustrating.
• 18:47
  And yet we force our frontline
• 18:49
  to do a lot of mundane things.
• 18:51
  And here they are highly qualified nurses and caregivers
• 18:54
  and we are forcing them to pull data from spreadsheets
• 18:57
  and cut and paste into things.
• 18:59
  Having the staff feel overwhelmed all the time where
• 19:03
  they're reacting to issues one at a time, somehow getting
• 19:08
  through it only to have the next issue and the next issue
• 19:11
  and the next issue come at, come at them like a tsunami.
• 19:14
  They somehow power through it, get through the day,
• 19:17
  and then like groundhog data will happen again tomorrow.
• 19:20
  That is stressful. Imagine if they could anticipate the
• 19:23
  issues, prepare for it in advance
• 19:25
  and not have to deal with it, right?
• 19:27
  Just think about going through life.
• 19:28
  If everything was a surprise to you,
• 19:30
  life would be a lot more stressful than if you could plan
• 19:33
  what's happening today, tomorrow, next week, next month.
• 19:36
  And then finally, they are forced to make decisions
• 19:39
  that are not just minor decisions.
• 19:42
  They impact a patient's health, maybe their life,
• 19:44
  and they don't have all the information at their fingertips.
• 19:47
  They don't have access to everything they need.
• 19:49
  And that creates anxiety that I am,
• 19:51
  I sure I'm doing the right thing.
• 19:52
  And, and so we've got many things
• 19:55
  that create the perfect storm from the manual tasks
• 20:00
  to the being overwhelmed to, you know,
• 20:02
  not having all the information at your fingertips
• 20:04
  that causes the, the stress.
• 20:06
  And we think that our approach to it can, uh, uh, you know,
• 20:10
  can solve many of the problems.
• 20:11
  Obviously we can't solve every problem in front of them,
• 20:14
  but we can do a lot to alleviate,
• 20:16
  alleviate the stress in the burnout.
• 20:19
  - But, but your point on this concept
• 20:20
  of living in a relatively constant fire trail,
• 20:23
  and it's not always the case, but it's,
• 20:25
  but it's a lot the case reminds you of
• 20:27
  where when travel goes wrong
• 20:29
  and how stressed out the staff
• 20:30
  is 'cause travel has gone wrong.
• 20:32
  'cause they're dealing with every single person trying
• 20:33
  to change and figure out what flight to get on,
• 20:35
  and they don't necessarily have all the logistics to do it.
• 20:37
  Well, is is the situation you're talking about,
• 20:39
  that stress level is very constant in,
• 20:41
  in large health systems.
• 20:43
  And so your point on how can we make that more planned,
• 20:46
  more, more systematic, a a little bit less stress,
• 20:49
  a little bit less fire drills goes a long way in terms
• 20:52
  of making just the environment easier
• 20:53
  to work in? , you're
• 20:55
  - Exactly right, Scott.
• 20:56
  They, they call it, uh, airlines, call it iop,
• 20:58
  irregular operations.
• 21:00
  So when, uh, there's a storm in Chicago
• 21:01
  and 2000 people have missed their connecting flights
• 21:04
  and you need to rebook them,
• 21:06
  there's a whole different method that comes into play
• 21:09
  where the airline is in IOP mode,
• 21:11
  where they're in irregular operations
• 21:13
  and their goal is to get out of IO into regular operations
• 21:16
  as quickly as they can.
• 21:17
  And your point's exactly right,
• 21:19
  health systems are often operating always in IOP mode,
• 21:23
  and that's what, uh, is stressful.
• 21:26
  - That sounds exactly right.
• 21:28
  You and a colleague,
• 21:29
  co-authored the book on hospital operations,
• 21:32
  a brilliant book on Better Healthcare
• 21:34
  through math is I think the title of, I've got that right.
• 21:37
  Is it, is that right? That's right.
• 21:39
  In in the year ahead,
• 21:41
  what do you anticipate will be the biggest,
• 21:44
  the single biggest challenge that
• 21:46
  that hospital executive teams and leadership teams face?
• 21:49
  And, and, and how can leaders stay ahead of that now?
• 21:51
  What, what are the biggest challenges
• 21:52
  or the biggest challenge that
• 21:53
  operations teams are gonna face?
• 21:55
  - Yeah, so staying on the operational side,
• 21:56
  because obviously clinical, there's a whole bunch of things.
• 21:58
  We are not expert on the clinical,
• 22:00
  but if I think about the single biggest challenge from an
• 22:03
  operational perspective for health system leaders is
• 22:07
  to do more with the existing assets
• 22:10
  and the existing staff that are already in place now, right?
• 22:14
  In the past, health systems could simply build their way
• 22:17
  out of operational problems.
• 22:18
  When they ran into operational problems,
• 22:20
  they constructed more hospitals, put in more ORs,
• 22:23
  put in more inpatient units,
• 22:25
  built ambulatory facilities, they kept building.
• 22:28
  Now they are under enormous pressure from many directions.
• 22:32
  Demand is going up. We've got a aging population,
• 22:34
  more chronic illnesses, reimbursement levels are down,
• 22:38
  input costs, particularly labor are rising like crazy.
• 22:41
  So that makes it, uh, that makes it particularly hard.
• 22:45
  And the result of all
• 22:46
  of these forces is the financial performance
• 22:48
  of most health systems has deteriorated right now.
• 22:52
  Therefore, they have to do more with less.
• 22:53
  If you look at asset utilization broadly
• 22:57
  measured in healthcare,
• 22:59
  it lags every other asset intensive service
• 23:02
  business by a lot.
• 23:04
  Now, some of it's not controllable.
• 23:06
  Other service businesses can operate
• 23:07
  24 hours around the clock.
• 23:09
  Healthcare essentially is at best a 12 hour operation.
• 23:12
  It's a 7:00 AM to 7:00 PM kind of operation.
• 23:15
  So by definition, if you are perfect,
• 23:17
  you can be 50% utilized.
• 23:18
  So that's, uh, you know, a starting point that's,
• 23:21
  that's difficult, but they can do much better.
• 23:25
  And if you looked at what is the value at stake,
• 23:28
  there are 5,000 hospitals in this country.
• 23:30
  On average, they have three
• 23:31
  or $400 million in assets, not counting real estate.
• 23:35
  So an or is 10
• 23:36
  or $15 million, an imaging machine is three or $4 million.
• 23:39
  Inpatient units are expensive.
• 23:41
  Three or $400 million per hospital across 5,000 hospitals
• 23:46
  is $2 trillion of assets.
• 23:49
  If you unlock 10 points of productivity,
• 23:52
  you can pump $200 billion of value
• 23:55
  per year into this country's healthcare system, right?
• 23:58
  That means it's 30 or 40 million per hospital, which is
• 24:02
  straight bottom line effects.
• 24:04
  And if you did that, it's a boost to the bottom line.
• 24:07
  But more importantly, it has the ability
• 24:10
  to serve patients more effectively, more access,
• 24:12
  better service, faster throughput, less wait time.
• 24:15
  And so the value at stake is enormous.
• 24:18
  And that's kind of our sole focus on what we're trying
• 24:21
  to deliver to our customers every day.
• 24:24
  - No, thank you for that.
• 24:25
  I mean, uh, unlocking that value in those assets
• 24:27
  that are already there versus building more assets,
• 24:30
  you know, and, and, and tying it back
• 24:32
  to the patient, into the workforce.
• 24:35
  This critical concept of reducing burnout
• 24:39
  by making life
• 24:41
  and work a little bit more manageable by,
• 24:43
  by having better predictability, better analytics,
• 24:46
  better recommendations on how to, how to manage operations
• 24:50
  or, or, or, or better tools is so important.
• 24:54
  Mohan, as always, I want to thank you
• 24:56
  for taking the time to visit with you.
• 24:58
  To me, I always learn a lot when I speak to you
• 25:01
  and your leadership about sort of hospital operations and,
• 25:05
  and how, you know, the supply demand,
• 25:07
  the logistics can be better managed and,
• 25:10
  and how you're doing that at 5,000 plus operating rooms,
• 25:12
  hundreds and hundreds of infusion
• 25:14
  centers and, and a lot more.
• 25:15
  Thank you for joining us on this discussion, sort
• 25:18
  of AI in the workforce, empowering staff
• 25:21
  and systems to thrive.
• 25:23
  So, so important to, to make things more predictable,
• 25:25
  getting outta what you call this IROC concept,
• 25:28
  this a regular operations concept of a constant fire drill,
• 25:31
  which causes so much, so much stress and so much burnout.
• 25:34
  Moan, thank you very much for, for visiting. Thank you,
• 25:37
  - God, great to chat with you as always.
• 25:38
  Thank you.
• 25:40
  - Well, thanks again to our listeners for tuning in today.
• 25:43
  I'd also like to thank Lean Toss
• 25:44
  for sponsoring this episode.
• 25:46
  You can tune into more podcasts from Becker's Healthcare
• 25:49
  by visiting our podcast page at becker's podcast.com.