April 23, 2026 • 40 mins
Your best employee might be 30 days from quitting and the evidence could be sitting in plain sight inside scheduling software. We dig into the senior living labor crisis and the uncomfortable reality that turnover is not just a people problem, it is a math problem with brutal second-order effects: agency premiums, productivity loss, manager time drained into chaos, and even resident move-outs that can erase tens of thousands in revenue.
We walk through a privacy-first approach to predictive retention, where AI estimates 30, 60, and 90-day flight risk using operational signals already generated by payroll and scheduling systems. No reading texts. No keystroke logging. No GPS stalking. Instead, the model looks for meaningful deviations like sudden shift swaps, changes in overtime behavior, time since last raise, and pay compared to local market benchmarks. The goal is supportive action, not punishment: the right check-in, schedule fix, or compensation move before someone mentally checks out.
Then we zoom out to the bigger redesign: remote patient monitoring and ambient sensors that reduce exhausting rounds and enable acuity-based staffing, plus the real-world pitfalls like alert fatigue. We also connect retention to purpose and culture through outcomes dashboards, community health workers handling SDOH needs, PACE partnerships, telehealth coverage, and systems that measure manager quality while routing family praise to the people who earned it. If AI can predict burnout and quitting in senior care, what happens when it spreads to every other industry?
Subscribe for more deep dives, share this with a leader who owns staffing, and leave a review if you want more reporting on AI, workforce analytics, and the future of care. What part of this future feels helpful to you, and what part crosses the line?
Listen
Watch
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
SPEAKER_01 (00:00):
Imagine knowing with like 89% certainty that your
absolute best employee is gonnaquit their job.
SPEAKER_00 (00:06):
Right.
SPEAKER_01 (00:06):
And you know this uh a full month before they even
make the conscious decision todo it themselves.
Yeah.
SPEAKER_00 (00:13):
It's wild.
SPEAKER_01 (00:14):
Which is crazy.
You haven't read their personaltext messages, you haven't, you
know, listened in on their breakroom conversations, you haven't
violated their privacy in anyway whatsoever.
Instead, you've just beenquietly observing the uh the
invisible, completelyoperational footprint that they
leave behind in your dailyscheduling software.
SPEAKER_00 (00:33):
Aaron Powell I mean when you put it like that, it
sounds like science fiction.
SPEAKER_01 (00:36):
Trevor Burrus Yeah, a little bit.
SPEAKER_00 (00:37):
Or like perhaps uh corporate surveillance dystopia,
right?
Trevor Burrus, Jr.
But it's actually a reality.
It is fundamentally rewritingthe operational playbook for
really one of the most strainedindustries in our entire
economy.
SPEAKER_01 (00:50):
Aaron Powell Absolutely.
SPEAKER_00 (00:50):
Aaron Ross Powell We're talking about predictive
retention.
SPEAKER_01 (00:53):
Aaron Ross Powell Well, welcome to a brand new
deep dive.
Whether you're listening to thisas, say, a facility operator
who's frantically trying tofigure out how you're going to
cover a 3 a.m.
memory care shift.
SPEAKER_00 (01:05):
Aaron Powell We've all been there.
SPEAKER_01 (01:06):
Trevor Burrus Right.
Or maybe you're a concernedfamily member trying to track
the quality of care your lovedones are getting, or you know,
just a deeply curious learnerwho is fascinated by massive
systemic problem solving.
SPEAKER_00 (01:20):
You're in the right place.
SPEAKER_01 (01:21):
You are in the exact right place.
Because today we are looking ata stack of incredibly detailed,
frankly, eye-opening sources.
SPEAKER_00 (01:29):
Yeah, we've got a lot on the desk today.
SPEAKER_01 (01:30):
We do.
Sitting right here, we haveindustry reports from A.
Shann Cal, Ziegler, Argentum,and the National Investment
Center.
SPEAKER_00 (01:37):
Which are huge players.
SPEAKER_01 (01:38):
Massive.
And right next to those, we havethis series of incredibly
comprehensive white papers fromSenior C R E.
They detail this platform calledthe Workforce Retention
Intelligence Engine.
SPEAKER_00 (01:49):
Or uh WRIE for short.
SPEAKER_01 (01:51):
W-R-I-E, yeah.
And so the mission for this deepdive is to unpack this massive
looming labor crisis in seniorcare and assisted living.
SPEAKER_00 (02:01):
But we aren't just going to sit here and admire the
problem.
SPEAKER_01 (02:03):
No, definitely not.
We are exploring a completeparadigm shift today.
We are looking at how an entireindustry is attempting to move
away from this frantic,financially ruinous cycle of
constantly replacing burned-outstaff.
SPEAKER_00 (02:18):
Aaron Powell Exactly.
The data we're looking atdetails a shift toward using
proactive intelligence toprevent turnover before it ever
happens.
SPEAKER_01 (02:26):
Right.
SPEAKER_00 (02:27):
And in the process, completely redesigning the
physical and, well, theemotional nature of the work
itself.
SPEAKER_01 (02:32):
Aaron Powell Which is so needed.
And just to set the stage rightout of the gate for you
listening, we are exploring thistopic purely based on the data
provided in these sources.
SPEAKER_00 (02:41):
Aaron Powell Right, strictly the data.
SPEAKER_01 (02:42):
Aaron Powell Strictly the data.
We're analyzing how a vitalsector is attempting to
completely rewire itsoperational DNA just to survive.
We aren't here to, you know,debate the broader politics of
healthcare or labor.
SPEAKER_00 (02:54):
No, we're just following the numbers, the
technology, and the outcomes.
SPEAKER_01 (02:57):
Aaron Powell Exactly.
And before we can even begin tounderstand this AI-driven
solution, I mean we really haveto grasp the sheer scale of the
crisis first.
SPEAKER_00 (03:05):
Oh, absolutely.
SPEAKER_01 (03:06):
We have to look at the flawed mental models that
operators have been using tofight it.
So let's start with thedemographic cliff.
SPEAKER_00 (03:13):
Aaron Powell Yeah, the demographic cliff is like
the necessary foundation forthis entire conversation.
Because the map here is it'ssimply staggering.
SPEAKER_01 (03:23):
It really is.
SPEAKER_00 (03:24):
So according to the data from the National
Investment Center in Argentum,the senior living industry is
going to need 660,000 moreworkers by 2033.
SPEAKER_01 (03:35):
Wait, 660,000?
SPEAKER_00 (03:37):
Yeah.
And that is just to maintainbaseline operations.
SPEAKER_01 (03:40):
Just to keep the doors open and do what they're
doing right now.
SPEAKER_00 (03:43):
Exactly.
But when you zoom out to look atthe total new hires needed to
cover both industry growth andthe massive worker turnover.
SPEAKER_01 (03:52):
Because turnover is huge.
SPEAKER_00 (03:53):
Right.
The projections indicate a needfor over 1.3 million new hires
by 2030.
SPEAKER_01 (03:59):
Over a million new hires in just a few years.
SPEAKER_00 (04:02):
Yeah, let that sink in.
SPEAKER_01 (04:04):
I mean, let's think about the gravity of that number
for a second.
We are talking about attemptingto recruit a population roughly
the size of the entire city ofDallas into jobs that are
physically demanding,emotionally draining, and let's
be honest, historically lowpaying.
SPEAKER_00 (04:20):
Historically very low paying.
SPEAKER_01 (04:21):
And they have to do this while 63% of providers are
already facing intense staffingshortages right now.
Today.
SPEAKER_00 (04:28):
Right.
The sources note that 87% ofoperators are currently
reporting difficulty hiring.
87%.
SPEAKER_01 (04:36):
That's basically everybody.
SPEAKER_00 (04:37):
Pretty much.
Plus, the workforce itself isaging out.
A full 43% of the current staffare aged 50 and older.
SPEAKER_01 (04:45):
Wow.
So nearly half the workforce is50 plus.
SPEAKER_00 (04:49):
Exactly.
So you have a workforce that isnaturally aging out of the
physically demanding aspects ofthe job right at the precise
moment a massive generation ofseniors requires their care.
SPEAKER_01 (04:58):
It's the perfect storm.
SPEAKER_00 (05:00):
It really is.
And to understand why operatorsare failing to meet this moment,
we have to look at the turnoverrates.
SPEAKER_01 (05:05):
Oh, the turnover rates are brutal.
SPEAKER_00 (05:07):
We are talking about an industry-wide annual turnover
rate for frontline staff thatsits at an abysmal 50 to 80
percent.
SPEAKER_01 (05:15):
Wait, 80 percent, like annually.
SPEAKER_00 (05:17):
Annually.
A facility could literally bereplacing 80 percent of its
direct care staff every single12 months.
SPEAKER_01 (05:24):
That is insane.
It's like it's like trying tofill a bathtub with a massive
hole in the bottom.
SPEAKER_00 (05:29):
That's a great analogy.
SPEAKER_01 (05:30):
Right.
But instead of fixing the leak,operators are just spending all
their capital buying bigger andbigger buckets to pour water in.
SPEAKER_00 (05:37):
Yep.
Which brings us to the financialmechanics of this crisis.
Because usually, you know, whenwe talk about a business's
profit and loss statement, thereis this expectation of
precision.
SPEAKER_01 (05:47):
Sure, you want the spreadsheet to balance.
SPEAKER_00 (05:49):
Right.
You look at the labor line on aspreadsheet, the math shows a
specific number, and it feelsclean, paid or unpaid.
SPEAKER_01 (05:56):
But I'm guessing it's not that clean here.
SPEAKER_00 (05:58):
Aaron Powell No, we like financial realities to be
visible and neatly categorizedinto tidy little rows.
But in senior care, the truecost of labor is completely
hidden beneath the surface.
SPEAKER_01 (06:08):
How so?
SPEAKER_00 (06:09):
It is the absolute definition of financial muddy
waters.
Because when you ask an operatorwhat it actually costs, when a
certified nursing assistant, aCNA, leaves their facility, the
visible cost, the number theyactually track on that clean
spreadsheet, is usually statedas thirty five hundred to five
thousand dollars.
SPEAKER_01 (06:28):
Aaron Powell Okay.
So three and a half to fivegrand.
That makes sense on paper.
I mean, I assume that covers therecruitment ads, the background
check.
SPEAKER_00 (06:35):
Right, the onboarding paperwork.
SPEAKER_01 (06:36):
Maybe a few weeks of shadowing.
SPEAKER_00 (06:38):
Exactly.
Those are the direct costs.
SPEAKER_01 (06:40):
Right.
SPEAKER_00 (06:40):
But the senior CRE sources provide a really
comprehensive accounting of thetrue cost, the all-in
replacement cost.
SPEAKER_01 (06:48):
Aaron Powell And this is where the traditional
financial model completely fallsapart, right?
SPEAKER_00 (06:51):
Completely.
Yeah.
Because you have to factor inthe indirect costs.
When that CNA leaves, theirshifts don't just magically
disappear.
SPEAKER_01 (06:58):
The patients still need care.
SPEAKER_00 (07:00):
Exactly.
Yeah.
So the facility is forced to payyou forty to eighty percent
premium to a temporary agencyjust to bring in a stop cap
worker.
SPEAKER_01 (07:08):
Oh wow.
So they're paying almost doublejust to put a body in the
building, the bigger bucket forthe leaky bathtub.
SPEAKER_00 (07:13):
Precisely.
And it doesn't stop with agencypremiums either.
There is a massive productivityloss.
SPEAKER_01 (07:20):
Because the new person doesn't know the ropes.
SPEAKER_00 (07:22):
Exactly.
A brand new CNA, even anexperienced one who is just new
to this specific building,operates at only 60 to 70%
efficiency for their first 60 to90 days.
SPEAKER_01 (07:34):
That makes total sense.
They don't know the facilitylayout.
SPEAKER_00 (07:37):
They don't know which residents prefer their
medication crushed inapplesauce.
SPEAKER_01 (07:40):
Right.
Or they don't know the subtleinterpersonal dynamics of the
floor, like who gets along withwho.
SPEAKER_00 (07:45):
Exactly.
So everything takes longer.
Then you have the manager timediverted away from patient care
and into HR crisis management.
Trevor Burrus, Jr.
SPEAKER_01 (07:53):
Conducting interviews.
Yeah.
SPEAKER_00 (07:55):
Managing the chaotic schedule.
SPEAKER_01 (07:57):
Right.
And then there's the ultimatehidden cost mentioned in the
data, which just blew my mind.
Resident move out.
SPEAKER_00 (08:03):
Oh, this is massive.
SPEAKER_01 (08:05):
The sources note that resident dissatisfaction,
which is almost always driven byinconsistent care from a
rotating cast of stressed-outtemporary workers, can actually
result in a move out.
SPEAKER_00 (08:14):
Yeah, families notice when there's a new face
every week.
SPEAKER_01 (08:17):
And they don't like it.
And a single resident departurecan mean$40,000 to$80,000 in
lost revenue.
SPEAKER_00 (08:24):
It's a huge hit.
SPEAKER_01 (08:25):
So one frustrated nurse quitting could literally
trigger a chain reaction thatcosts a facility a year's worth
of profit.
SPEAKER_00 (08:32):
Which brings the true cost of a single CNA
turnover event into very starkrelief.
When you roll all of thoseindirect and direct costs
together, the real number isactually$8,000 to$15,000 per
CNA.
SPEAKER_01 (08:47):
Wow.
Up to$15,000.
SPEAKER_00 (08:49):
Yeah.
And for a registered nurse inRN, the true replacement cost is
$40,000 to$60,000.
SPEAKER_01 (08:55):
Let's let's actually do the math on that for a
standard 100-bed facility,because I think this is where it
gets real.
Let's do it.
Okay.
Let's say they have 50 directcare staff members.
If they have a 60% turnoverrate, which, you know, we said
is completely average for thisindustry, that means they are
replacing 30 employees everysingle year.
SPEAKER_00 (09:14):
30 people a year, yeah.
SPEAKER_01 (09:15):
Even if we use a super conservative$4,000 per
turnover and just direct costs,they are bleeding$120,000 a
year.
SPEAKER_00 (09:22):
Just indirect visible costs.
SPEAKER_01 (09:25):
Right.
But if we use the true all-incost of up to$15,000 per
departure, I mean, we aretalking about nearly half a
million dollars vanishing intothin air every single year.
SPEAKER_00 (09:37):
Just to maintain the exact same level of inadequate
staffing.
SPEAKER_01 (09:41):
It fundamentally changes the narrative around
wages, doesn't it?
SPEAKER_00 (09:44):
It really does.
SPEAKER_01 (09:45):
Wait, hold on.
This is where the logic seemscompletely broken to me.
If you talk to operators, theirimmediate default defense is
always, well, we simply cannotpay competitive wages because
our operating margins are tootight.
SPEAKER_00 (09:57):
You hear that all the time.
SPEAKER_01 (09:58):
Right.
But if we look at these numbers,aren't the margins incredibly
tight specifically becausethey're constantly bleeding half
a million dollars a yearreplacing people?
SPEAKER_00 (10:06):
Yes.
SPEAKER_01 (10:07):
Isn't the low wage strategy the exact thing
destroying their margins in thefirst place?
SPEAKER_00 (10:12):
That is the crucial realization the data points us
toward.
The industry is trapped in asystemic doom loop.
SPEAKER_01 (10:19):
A doom loop.
SPEAKER_00 (10:20):
Yeah.
Think about it.
People leave because the work isdemanding, the wages are low,
and they are burned out.
Their departure forces theoperator to hire agency staff at
an exorbitant 80% premium.
That agency spend shrinks theoperating margins even further.
SPEAKER_01 (10:38):
Because they're bleeding cash.
SPEAKER_00 (10:39):
Right.
And because the margins areshrunk, the operator genuinely
feels they cannot afford BATHERwage increases for their
permanent loyal staff.
SPEAKER_01 (10:48):
Oh man.
Which leads to more low wages,more burnout, and more people
quitting.
SPEAKER_00 (10:53):
It's a self-reinforcing cycle of
financial disaster.
SPEAKER_01 (10:56):
It really is.
And the core thesis embedded inthese senior CRE white papers is
that retention is no longer justlike a soft HR function.
SPEAKER_00 (11:04):
No, it's not about pizza parties or employee of the
month plaques anymore.
SPEAKER_01 (11:08):
Thank goodness.
Retention is the ultimatefinancial strategy.
Reducing turnover is the primarymechanism to actually fund the
wage increases that theworkforce is rightfully
demanding.
SPEAKER_00 (11:18):
You have to patch the leak to afford the water.
SPEAKER_01 (11:20):
Exactly.
So how exactly do you patch aleak before the pipe even
bursts?
Because human behavior isnotoriously unpredictable, or at
least we like to think it is.
SPEAKER_00 (11:29):
We do like to think that.
SPEAKER_01 (11:30):
This leads us to the massive conceptual shift
introduced by the WRIE platform,the workforce retention
intelligence engine.
This is an AI system designed toliterally predict when someone
is going to quit.
SPEAKER_00 (11:44):
It is.
The platform analyzes over 40distinct behavioral and
operational signals to predictturnover risk.
And it doesn't just guess.
You know, it models out risk on30, 60, and 90-day horizons.
SPEAKER_01 (11:57):
And the predictive accuracy is what initially
caught my eye in these reports.
The sources state that for a30-day departure risk, the
accuracy is 89%.
SPEAKER_00 (12:06):
89%.
It's incredibly high.
SPEAKER_01 (12:08):
That's almost certain.
SPEAKER_00 (12:09):
Yes.
And it achieves this by treatingthese horizons as escalating
intervention points.
Aaron Powell, Jr.
SPEAKER_01 (12:13):
What does that mean?
SPEAKER_00 (12:14):
So the 90-day risk acts as an early warning signal.
It allows for proactive cultureimprovements or maybe just
subtle check-ins from a manager.
SPEAKER_01 (12:21):
Like, hey, how are things going?
SPEAKER_00 (12:23):
Exactly.
Then the 60-day risk triggerselevated concern and maybe a
review of their upcomingschedule.
By the time someone hits the30-day risk threshold, the data
suggests the employee is showingstrong active departure
indicators.
SPEAKER_01 (12:37):
Aaron Powell Like they've already checked out
mentally.
SPEAKER_00 (12:39):
Right.
And at that point, immediatetargeted intervention is
required to save them.
SPEAKER_01 (12:44):
I have to admit, when I first read the phrase AI
analyzing my flight risk, mybrain immediately went to a very
dark, very dystopian place.
SPEAKER_00 (12:53):
Oh, of course it did.
SPEAKER_01 (12:54):
I pictured a manager tracking my bathroom breaks or
monitoring my personal textmessages or, you know, reading
my emails to see if I wassending a resume out.
How does a facility trackbehavior this closely without
completely destroying whateverfragile trust is left between
management and the staff?
SPEAKER_00 (13:12):
It's a vital question.
And the sources address thishead-on through what they term
privacy-first design and strictethical guardrails.
SPEAKER_01 (13:20):
Okay, good.
SPEAKER_00 (13:20):
The critical distinction here is the
difference between invasivesurveillance and supportive
operational intelligence.
SPEAKER_01 (13:26):
Okay, so what does that distinction actually look
like in the code?
How does it operate day-to-day?
SPEAKER_00 (13:31):
First off, W-R-I-E explicitly excludes all
protected classes from itsalgorithms.
It is completely blind to age,race, gender, and religion.
SPEAKER_01 (13:41):
Aaron Powell That's essential.
SPEAKER_00 (13:42):
Definitely.
But more importantly, itabsolutely forbids invasive
surveillance.
There is no keystroke loggingwhatsoever.
SPEAKER_01 (13:50):
Thank goodness.
SPEAKER_00 (13:51):
Right.
There is no monitoring ofpersonal devices or company
phones.
There is no social mediascraping, no reading of personal
emails, and absolutely no GPStracking outside of the physical
facility.
SPEAKER_01 (14:02):
Okay, so if it's not reading my text to see if I'm
angry about my boss, and it'snot tracking my location to see
if I'm at a job interview acrosstown, how on earth does it know
I'm going to quit with 89%accuracy?
SPEAKER_00 (14:14):
By analyzing the operational data that is already
naturally generated by thefacility's existing management
systems.
SPEAKER_01 (14:20):
What kind of data?
SPEAKER_00 (14:21):
It looks at the footprint an employee leaves in
the scheduling and payrollsoftware.
For example, it tracksscheduling patterns.
Has an employee suddenly startedrequesting an unusual number of
shift swaps?
SPEAKER_01 (14:32):
Oh, interesting.
SPEAKER_00 (14:33):
Right.
Are they calling out on Mondaysconsistently?
Have their schedule preferenceschanged rapidly?
Or very tellingly, has anemployee who historically always
accepted overtime shiftssuddenly started refusing them?
SPEAKER_01 (14:45):
Ah, okay.
So it's looking for a deviationfrom their personal baseline.
SPEAKER_00 (14:49):
Exactly.
SPEAKER_01 (14:49):
So if I usually jump at time and a half pay, but
suddenly I'm saying no to everysingle extra shift, the system
flags that my motivation or myenergy has fundamentally
shifted.
SPEAKER_00 (14:59):
That's exactly how it works.
It also ingests compensationsignals.
SPEAKER_01 (15:03):
Which means what?
Exactly.
SPEAKER_00 (15:04):
It looks at the time elapsed since their last raise,
and it compares their currentpay rate to real-time market
benchmarks.
SPEAKER_01 (15:12):
Oh, wow.
SPEAKER_00 (15:13):
Yeah.
So if a CNA hasn't had a raisein 18 months, and the system
knows that the fast foodrestaurant down the street just
raised their starting wage to$18an hour, the risk score
elevates.
SPEAKER_01 (15:24):
Because the system knows they could literally walk
across the street and make moremoney flipping burgers with way
less stress.
SPEAKER_00 (15:30):
Exactly.
And the outcome of thesepredictions is crucial to
understand.
The system is designed to bepurely supportive, never
punitive.
SPEAKER_01 (15:37):
That's a fine line to walk.
SPEAKER_00 (15:39):
It is, but the scores are only visible to
direct supervisors and HR.
The goal is to trigger anintervention that helps the
employee, not punishes them forbeing unhappy.
SPEAKER_01 (15:49):
Okay, so the system is gathering the scheduling and
compensation data ethically, andit's mapping out long-term
flight risk.
Right.
But let's bring this down to theground level.
How does this actually play outin reality on a random Tuesday
morning at 5 a.m.
when total chaos strikes andsomeone calls in sick?
SPEAKER_00 (16:06):
The dreaded 5 a.m.
call out.
SPEAKER_01 (16:08):
Exactly.
Knowing someone might quit in 90days is great, sure, but a
manager still has to staff thefloor in two hours.
SPEAKER_00 (16:15):
And this is where we see the transition from
long-term retention to immediateoperational triage.
The platform features apredictive call-out engine
designed specifically for thisexact scenario.
SPEAKER_01 (16:27):
A predictive call-out engine.
SPEAKER_00 (16:29):
Yeah.
It aims to forecast staffinggaps 24 to 72 hours before they
actually happen.
SPEAKER_01 (16:34):
Aaron Powell And the sources claim it does this with
72% accuracy.
That's that's wild.
It knows someone is going tocall out sick three days before
they even wake up with a sorethroat.
How is that mechanicallypossible?
SPEAKER_00 (16:44):
Aaron Powell The machine learning models ingest
18 distinct data signals,looking back over 18 months of
historical facility data.
SPEAKER_01 (16:51):
Aaron Powell Eighteen signals, like what?
SPEAKER_00 (16:53):
Well, some of the inputs are quite logical.
It pulls in weather forecasts,knowing that a predicted massive
snowstorm or a severe heat wavecorrelates with a spike in
absences.
SPEAKER_01 (17:03):
Okay.
Yes.
Snowstorms equal call-outs, thatmakes sense.
SPEAKER_00 (17:06):
Aaron Powell It also looks at local event calendars
and recurring historicalpatterns, what the industry
calls Monday spikes or holidayweekend drop-offs.
SPEAKER_01 (17:14):
Aaron Powell But it goes deeper than just knowing
it's gonna snow or that it's aholiday, right?
The white paper mentions thatthe AI models team dynamics and
notes that some call outs areactually contagious.
SPEAKER_00 (17:25):
Contagious call outs, yes.
SPEAKER_01 (17:26):
Aaron Powell This was a massive aha moment for me.
Does the system actually knowthat if Sarah calls out on
Monday, John is statisticallylikely to call out on Tuesday?
SPEAKER_00 (17:34):
Yes.
And understanding the mechanismbehind that is really
fascinating.
The machine learning algorithmis designed to identify hidden
historical patterns that a humanmanager who is, you know, bogged
down in daily crisis managementwould simply miss.
SPEAKER_01 (17:49):
Right.
They're too busy putting outfires.
SPEAKER_00 (17:51):
Exactly.
A human manager might be toobusy to realize that over the
last year, whenever the dementiaunit is short staffed by one
person, a specific second personalmost always calls out the very
next day.
SPEAKER_01 (18:04):
Because the physical workload of covering for that
missing person was so punishingthat the second person is just
physically and emotionallydestroyed by the end of their
shift.
SPEAKER_00 (18:12):
Yep.
SPEAKER_01 (18:13):
It's a domino effect of exhaustion.
SPEAKER_00 (18:15):
Precisely.
The AI maps these team cohesionscores and tracks high-risk
pairings.
It understands the operationalfriction between different SAV
members and different residentacuities.
SPEAKER_01 (18:26):
That is incredible.
SPEAKER_00 (18:27):
And think about the massive financial and
operational impact of knowingthis in advance.
Without this system, a managerfinds out about a call-out two
hours before a shift begins.
SPEAKER_01 (18:37):
And they panic.
SPEAKER_00 (18:38):
Total panic.
They pull out a binder, make adozen phone calls to staff who
do not want to answer theirphones at 5 a.m.
SPEAKER_01 (18:44):
I certainly wouldn't.
SPEAKER_00 (18:45):
Right.
And ultimately, they're forcedto hire a temp agency worker at
that 80% premium just tomaintain legal compliance on the
floor.
SPEAKER_01 (18:54):
They are forced to buy the bigger bucket again.
SPEAKER_00 (18:56):
But if the system predicts a high probability of a
gap 72 hours out, everythingchanges.
The system automaticallysurfaces coverage options.
SPEAKER_01 (19:05):
Automatically?
SPEAKER_00 (19:06):
Yes.
It can activate a standbyinternal slope pool at normal
base wages.
It can send targeted textmessages offering an open shift
specifically to staff memberswho the system knows are not at
risk of hitting overtime pay.
SPEAKER_01 (19:20):
Oh, that's smart.
So it avoids paying time and ahalf if it doesn't have to.
SPEAKER_00 (19:23):
Exactly.
It completely removes themanager from the frantic manual
phone tree process andintercepts the exorbitant agency
spend before the crisis evenmaterializes.
SPEAKER_01 (19:34):
Aaron Powell, which leads directly into the
platform's retention ROI engine.
Because, you know, predicting acall out saves money today, but
the facility still has to provethat these broader interventions
are actually fixing the doomloop over time.
SPEAKER_00 (19:46):
Aaron Powell Right.
You have to prove the long-termvalue.
SPEAKER_01 (19:48):
And this part of the platform tracks the literal
financial return on investmentfor retention efforts.
It benchmarks compensation usingreal-time data from the Bureau
of Labor Statistics and theAHCA.
SPEAKER_00 (19:58):
So an operator is never guessing if their wages
are competitive.
They can see exactly where theysit relative to the local
market.
SPEAKER_01 (20:05):
And it calculates that$8,000 to$15,000 turnover
cost we discussed earlier, butit models it out for every
specific role in that specificbuilding.
SPEAKER_00 (20:13):
It makes the invisible PL visible.
SPEAKER_01 (20:15):
Yes.
But it goes beyond justmeasuring the problem.
It triggers automated actionstoo.
For instance, it manages a peerrecognition and kudo system.
SPEAKER_00 (20:24):
I love this feature.
SPEAKER_01 (20:25):
Me too.
Staff can award points to eachother for helping out with a
difficult lift or, you know,covering a break.
And the data shows that thisspecific type of peer-to-peer
recognition directly reduces90-day attrition rates.
SPEAKER_00 (20:38):
Because feeling valued by your peers is
incredibly powerful.
SPEAKER_01 (20:42):
Definitely.
And the overall result ofoperators utilizing this kind of
predictive scheduling and ROItracking, a 23% annual turnover
reduction.
SPEAKER_00 (20:52):
That is a massive operational victory.
23% is huge.
SPEAKER_01 (20:56):
It really is.
SPEAKER_00 (20:56):
It is significant.
But I think we have to look atthe broader reality of the job
here.
Predicting when staff will leaveor perfectly optimizing the
schedule, that's only one pieceof a very complex puzzle.
True.
Because if the physical jobitself remains fundamentally
impossible or just incrediblyinefficient in grueling,
(21:16):
employees are eventually goingto leave no matter how
seamlessly you schedule theirshifts.
SPEAKER_01 (21:21):
Right.
You cannot retain people if thephysical environment they work
in is a nightmare.
SPEAKER_00 (21:26):
Exactly.
SPEAKER_01 (21:26):
Which brings us to a massive shift in how the work is
actually performed.
How does technology change thephysical daily reality of
caregiving?
SPEAKER_00 (21:36):
Well, this represents a vital evolution
from a reactive observationmodel to continuous passive
surveillance of the residents.
SPEAKER_01 (21:41):
Reactive observation meaning what?
SPEAKER_00 (21:43):
Traditionally, a CNA operates on a model of physical
rounds.
Every two hours, they walk downa long hallway, they open doors,
they knock, they peek in tocheck on residents, and then
they mark a chart.
SPEAKER_01 (21:54):
So instead of a security guard patrolling an
empty building every two hours,just hoping they happen to spot
something wrong at the exactExact moment they walk by.
What is the technologicalalternative?
SPEAKER_00 (22:03):
Aaron Powell The alternative is the remote
patient monitoring and sensorlayer, RPM.
Instead of relying purely onhourly physical rounds, the
facility installs passivesensors.
SPEAKER_01 (22:13):
Aaron Powell And let's clarify what we mean by
sensors here.
SPEAKER_00 (22:16):
Yes.
Very important.
We are not talking aboutinvasive cameras in bedrooms or
bathrooms.
SPEAKER_01 (22:20):
No one wants that.
SPEAKER_00 (22:21):
No.
And we aren't talking aboutwearable pendants or bracelets
that residents frequently takeoff, lose, or forget to charge.
SPEAKER_01 (22:29):
We are talking about ambient intelligence built into
the room itself floor vibrationsensors, bed sensors, room-based
radar systems.
SPEAKER_00 (22:36):
Correct.
And the capabilities outlined inthe white papers are just
remarkable.
Take the bed sensors thatmonitor sleep quality, for
example.
Okay.
By continuously analyzing sleeppatterns, the system can
actually spot the onset of aurinary tract infection, a UTI,
or a cardiac event 48 to 72hours early.
SPEAKER_01 (22:54):
Okay, stop right there.
How does a sensor under amattress or a vibration sensor
on the floor know that anelderly resident has a bacterial
infection?
That sounds like magic.
SPEAKER_00 (23:07):
It really does, but it comes down to establishing a
baseline of normal behavior andthen mathematically analyzing
subtle behavioral deviationsfrom that baseline.
SPEAKER_01 (23:16):
Okay, walk me through that.
SPEAKER_00 (23:17):
So a urinary tract infection in an elderly patient
doesn't always presentimmediately with a fever or
pain.
It often presents with subtlebehavioral changes.
SPEAKER_01 (23:26):
Like what?
SPEAKER_00 (23:26):
The floor sensors might detect a 30% increase in
bathroom visit frequency duringthe night.
SPEAKER_01 (23:31):
Oh, because they're getting up more often.
SPEAKER_00 (23:33):
Right.
And the bed sensor detects anincrease in restlessness,
tossing and turning, and subtlechanges in resting respiration
rates.
SPEAKER_01 (23:39):
So the system aggregates all those tiny
behavioral clues, the frequenttrips to the bathroom, the poor
sleep, the heavier breathing.
SPEAKER_00 (23:46):
Exactly.
By passively tracking thosemetrics, the algorithm flags an
anomaly days before severeclinical symptoms, like a high
fever, a dangerous fall due toweakness, or severe confusion
actually manifest.
SPEAKER_01 (23:59):
That is amazing.
SPEAKER_00 (24:00):
And there is gate analysis as well.
SPEAKER_01 (24:02):
Gate analysis.
SPEAKER_00 (24:03):
Yeah, the floor sensors can analyze the physical
footfalls of a resident.
Wow.
If a resident's normal walkingspeed subtly slows down over a
week, or if they start draggingtheir left foot slightly, the
system flags an elevated fallrisk before the resident ever
actually trips.
SPEAKER_01 (24:22):
And this technological layer completely
upends the traditional staffingmath, doesn't it?
SPEAKER_00 (24:27):
It really does.
SPEAKER_01 (24:28):
Because if you have this ambient AI constantly
monitoring the rooms for falls,wandering, or emerging
infections, you eliminate theneed for those blanket hourly
manual checks for low-riskresidents.
SPEAKER_00 (24:40):
Exactly.
Which allows the facility tomove to what the sources call
acuity-based staffing.
SPEAKER_01 (24:45):
Let's break down the economics of acuity-based
staffing.
SPEAKER_00 (24:47):
Okay.
So the data illustrates that ona traditional manual memory care
unit, a facility might need aCNA ratio of one caregiver to
every six residents just tosafely monitor everyone and
prevent wandering.
SPEAKER_01 (24:59):
One to six.
SPEAKER_00 (24:59):
But when you blanket that unit with well-implemented
remote patient monitoring, thatratio can safely shift from one
to six up to one to nine.
(25:23):
Huge savings.
SPEAKER_01 (25:24):
And they achieve that financial savings without
compromising safety.
SPEAKER_00 (25:28):
In fact, the data argues that safety actually
improves.
SPEAKER_01 (25:31):
Really?
How so?
SPEAKER_00 (25:33):
Because the staff isn't wasting time checking on
sleeping healthy residents,they're available immediately
when something actually happens.
The average critical alertresponse time under this
sensor-driven system drops tojust 4.2 minutes.
SPEAKER_01 (25:45):
That's incredibly fast, but there is a massive
warning sign flashing in thesesources regarding human behavior
and technology.
The concept of alert fatigue.
SPEAKER_00 (25:54):
Oh, yes.
Alert fatigue is real.
SPEAKER_01 (25:56):
Because just like a car alarm that goes off every
time the wind blows, if thesystem isn't perfectly
calibrated, it becomescompletely useless.
SPEAKER_00 (26:03):
Exactly.
If a bed sensor goes off everysingle time a resident merely
shifts their weight, the nursingstaff will be bombarded with
hundreds of false positivealarms a night.
SPEAKER_01 (26:12):
Which would drive anyone crazy.
SPEAKER_00 (26:14):
Very quickly, human nature takes over.
They will simply start ignoringthe dashboard, or worse,
unplugging the system entirely.
SPEAKER_01 (26:21):
So the technology completely fails if it is
deployed purely as a rigidsurveillance tool.
Right.
If management uses it to punishstaff for missing an alert
rather than integrating it intoa redesigned workflow that
genuinely assists them, thestaff will just reject it.
SPEAKER_00 (26:37):
The goal isn't automation for the sake of
replacing humans.
The goal is the precisiondeployment of human attention.
SPEAKER_01 (26:44):
I like that phrase.
Precision deployment.
SPEAKER_00 (26:47):
The technology has to be individually calibrated to
each residence-specific baselineso that when an alarm rings, the
staff knows it is a genuine,actionable emergency.
SPEAKER_01 (26:58):
So we've put sensors in the rooms to stop the
physical exhaustion of walkingthe halls all night.
The technology makes thephysical work more efficient and
targeted.
Yes.
But what about the emotionalexhaustion?
Caregiving is deeply inherentlyemotional work.
A floor sensor doesn't make acaregiver feel like their job
actually matters.
SPEAKER_00 (27:13):
No, it doesn't.
SPEAKER_01 (27:14):
To retain people in a notoriously tough field, you
have to connect their dailyrepetitive tasks to a larger
sense of purpose and actualclinical outcomes.
This requires a completelydifferent operational shift.
SPEAKER_00 (27:28):
It is a profound conceptual shift in how we view
the physical building itself.
SPEAKER_01 (27:33):
How so?
SPEAKER_00 (27:34):
Well, traditionally, a senior care facility operates
as a closed, isolated silo.
The facility hires all thestaff, they provide 100% of the
care, and they attempt to manageevery single aspect of a
resident's life within their ownfour walls.
SPEAKER_01 (27:48):
But the WRIE platform advocates for
shattering that silo entirely.
It pushes for what they call acommunity integrated hub.
SPEAKER_00 (27:56):
A community integrated hub.
SPEAKER_01 (27:57):
That's Kin X employee retention directly to
clinical outcomes.
I want to dive into thepsychology of this for a second.
How does showing a frontline CNAa data dashboard actually change
their job satisfaction?
SPEAKER_00 (28:08):
Aaron Powell Because it elevates the nature of the
work from mere task completionto profound clinical purpose.
SPEAKER_01 (28:13):
Give me an example.
SPEAKER_00 (28:14):
So the platform features an outcomes dashboard
that tracks macrometrics likehospital readmission rates and
successful ER diversions.
Imagine you are a CNA.
Your job is incredibly hard.
Yesterday, you spent an hourpainstakingly coaxing a resident
with dementia to drink twoglasses of water because you
noticed they were lethargic.
SPEAKER_01 (28:34):
Which is not easy to do.
SPEAKER_00 (28:35):
Not at all.
But today, you look at a screenand see that your specific
attention to their hydrationdirectly resulted in a
successful hospital diversion.
SPEAKER_01 (28:44):
Oh wow.
So you see the direct linebetween your hard work and a
human being staying out of theemergency room.
SPEAKER_00 (28:50):
Precisely.
The data shows that when staffcan visualize their measurable
clinical impact, they staylonger.
This connection alone yields a31% reduction in hospital
readmission.
SPEAKER_01 (29:00):
31%.
SPEAKER_00 (29:01):
Yeah.
It proves to the workforce thatthey are clinicians, not just
task rabbits.
SPEAKER_01 (29:05):
But to achieve those outcomes without burning out the
already exhausted clinicalnurses, the platform introduces
the strategic use of acompletely different role.
Community health workers orCHWs?
SPEAKER_00 (29:16):
CHWs are an incredibly underutilized asset
in senior care.
SPEAKER_01 (29:20):
And the economics of their role are vital here,
right?
SPEAKER_00 (29:23):
Absolutely.
They operate at a wage point ofroughly$18 to$26 an hour.
Compare that to clinical RNs whomake anywhere from$28 to$35 an
hour, or much more if they areagency temps.
SPEAKER_01 (29:35):
So what exactly is a CHW doing to offload the
pressure?
The white paper details a CHWtask queue and something called
SDOH domain screenings.
SPEAKER_00 (29:46):
Right.
So STOH stands for SocialDeterminants of Health.
SPEAKER_01 (29:49):
Okay.
SPEAKER_00 (29:50):
These are the crucial non-medical factors that
deeply impact a resident'soverall health things like
social isolation, foodinsecurity, transportation
barriers, and physical safety intheir environment.
SPEAKER_01 (30:00):
The day-to-day living stuff.
SPEAKER_00 (30:02):
Exactly.
The WRIE system systematicallytracks screenings across these
domains.
By having dedicated CHWs handlethese vital social and
navigational tasks, it frees upthe highly paid, highly stressed
clinical nurses to focusexclusively on what only they
can do clinical nursing work.
SPEAKER_01 (30:19):
It's a brilliantly simple concept when you think
about it.
You are expanding the capacityand the reach of the care team
without massively inflating thenursing payroll.
And this community integrationgoes even further.
The sources detail integratingPACE partnerships and telehealth
into the core infrastructure.
SPEAKER_00 (30:36):
PACE stands for Programs of All-Inclusive Care
for the Elderly.
SPEAKER_01 (30:40):
Okay.
SPEAKER_00 (30:40):
By integrating PACE partnerships, a facility can
actually bring external,federally, or state funded care
staff right into the building tosupport residents.
SPEAKER_01 (30:50):
Oh, that's smart.
Use outside resources.
SPEAKER_00 (30:52):
Yes.
And treating telehealth as core,embedded infrastructure is
another game changer.
Imagine an overnight shift.
A resident spikes a fever.
SPEAKER_01 (31:02):
Usually you'd have to call someone in.
SPEAKER_00 (31:03):
Right.
Instead of waking up an on-callnurse who has to physically
drive to the facility at 2 a.m.,the overnight staff has 247
access to a virtual telehealthnurse.
They get immediate clinicaldecision support.
SPEAKER_01 (31:15):
And I noticed the sources heavily emphasize
changing the relationship withthe residents' families too.
SPEAKER_00 (31:20):
Oh, this is a big one.
SPEAKER_01 (31:21):
They advocate for treating family members as
trained care partners, providingthem with structured
orientations on how to activelysupport care plans during their
visits.
SPEAKER_00 (31:30):
Which makes a lot of sense.
SPEAKER_01 (31:31):
It does.
But it's a huge departure fromtreating families merely as
customers who only get a phonecall when something goes wrong
or a bill is due.
It really transforms thefacility into a community hub.
SPEAKER_00 (31:43):
It connects all the disparate dots.
When the heavy burden of care isstructurally shared among CHWs,
integrated external PACEpartners, on-demand telehealth
nurses, and engaged familymembers, the direct care staff
are far less likely to becrushed by the workload.
SPEAKER_01 (32:01):
So we have optimized the schedule using predictive
AI.
We have upgraded the physicalreality of the building with
floor sensors and remotemonitoring.
And we have connected the workto a larger clinical purpose
while bringing in communitysupport.
SPEAKER_00 (32:14):
Right.
We've done a lot.
SPEAKER_01 (32:15):
But let's be entirely real for a moment.
None of that matters.
Not the sensors, not thetelehealth, not the perfect
schedule.
If your direct boss is terrible.
SPEAKER_00 (32:23):
No, it doesn't.
SPEAKER_01 (32:24):
Or if you are physically exhausted to the
point of total collapse.
SPEAKER_00 (32:27):
There is a very old, very universally accepted truth
in human resources.
People leave managers, notcompanies.
SPEAKER_01 (32:34):
Aaron Powell It's a cliche because it's true.
SPEAKER_00 (32:36):
Exactly.
You can have the best technologyin the world, but a toxic
supervisor will still drive yourbest talent out the door.
The final phases of thisplatform operationalize that
truth by attempting to measuremanagement quality objectively.
SPEAKER_01 (32:50):
Which is hard to do.
But the sources outline a deeplyintegrated 360-degree feedback
system.
It allows for continuous,anonymous peer, subordinate, and
supervisor evaluations, and ittracks those trends over time.
SPEAKER_00 (33:04):
And it even includes a feature called family praise
routing.
SPEAKER_01 (33:07):
Family praise routing.
Tell me about that.
SPEAKER_00 (33:10):
It's a subtle but powerful cultural lever.
Usually, when a family member ishappy with the care their mother
received, they might tell theexecutive director or maybe drop
a generic thank you card at thefront desk.
SPEAKER_01 (33:21):
Yeah, that's pretty typical.
SPEAKER_00 (33:22):
The system changes that.
When positive feedback issubmitted, it doesn't go to a
generic inbox.
The system routes it directly tothat specific staff member's
supervisor and logs it in theirpermanent personnel file.
SPEAKER_01 (33:33):
Oh wow.
SPEAKER_00 (33:34):
It forces a systemic culture of recognition.
The platform then generates asupervisor effectiveness score
based on all of these inputs.
SPEAKER_01 (33:42):
And the results of measuring management quality are
undeniably clear.
The data notes that facilitieswith top cordile managers see
89% staff satisfaction.
SPEAKER_00 (33:52):
89%.
SPEAKER_01 (33:53):
To achieve 89% satisfaction in an industry
currently experiencing 80%turnover is nothing short of
miraculous.
SPEAKER_00 (34:01):
It proves mathematically that management
quality compounds.
Better supervisors retain betterstaff.
Better staff who know theresidents and aren't burned out
deliver better care.
Better care leads to fewerhospitalizations and higher
revenue.
SPEAKER_01 (34:15):
But then we reach the final layer, staff wellness.
And I have to admit, when Ifirst read this section, my
skepticism spiked.
SPEAKER_00 (34:22):
I'm not surprised.
SPEAKER_01 (34:23):
The white paper details the use of AWS real-time
burnout alerts and tracks acomposite score from zero to a
hundred to literally measure anemployee's fatigue.
Right.
Can an algorithm actuallymeasure something as deeply
personal, subjective, andemotionally complex as burnout?
Burnout is a feeling, it's astate of mind.
How on earth does a computerknow I'm burnt out before I even
(34:44):
realize it myself?
SPEAKER_00 (34:45):
It is a completely natural reaction to question the
mechanization of human emotion.
But the data here asks us tocompletely reframe how we view
burnout.
SPEAKER_01 (34:56):
Okay, reframe it how.
SPEAKER_00 (34:57):
It demands that we stop treating burnout as a
character flaw or a weakness orjust a passing bad mood.
It treats burnout as aphysiological clinical condition
that is driven by very specific,measurable operational data
points.
SPEAKER_01 (35:11):
So what exactly is this algorithm measuring to
generate that zero to a hundredfatigue score?
SPEAKER_00 (35:16):
Well, it tracks the raw physical toll of the
schedule.
How many consecutive shifts hasthis CNA worked?
It monitors overtime hoursmathematically.
Okay.
It analyzes shift patterns toidentify known fatigue
thresholds, like someone workinga night shift, having eight
hours off, and then working anevening shift.
SPEAKER_01 (35:33):
That's a brutal turnaround.
SPEAKER_00 (35:34):
But most impressively, it measures a
staff member's exposure to highacuity residents.
SPEAKER_01 (35:38):
Explain that acuity exposure.
How does it track that?
SPEAKER_00 (35:41):
The system knows the clinical difficulty of every
resident in the building.
It knows who requires atwo-person lift to get out of
bed.
It knows who exhibits combativebehaviors due to advanced
dementia.
Right?
The algorithm calculates howmany days in a row a specific
employee has been assigned tothe most physically and
emotionally demanding patientsin the building.
SPEAKER_01 (36:01):
So it is quantifying the actual physical and
emotional load placed on thehuman body and mind.
SPEAKER_00 (36:07):
Exactly.
SPEAKER_01 (36:08):
What happens when the math decides that load is
too heavy, when that zero to ahundred score gets too high?
SPEAKER_00 (36:14):
It triggers tiered systemic interventions.
If a staff member score crosses60, it's flagged as high risk.
SPEAKER_01 (36:21):
And what does high risk do?
SPEAKER_00 (36:23):
The system automatically sends a
notification to the departmentsupervisor, triggering a
mandatory workload review.
The supervisor is required tolook at the next week's schedule
and adjust the acuity load.
SPEAKER_01 (36:33):
And if the score goes over 80.
SPEAKER_00 (36:35):
A score over 80 is classified as critical risk.
At this point, the systemdoesn't just suggest an
intervention, it demands one.
SPEAKER_01 (36:43):
Demands one.
SPEAKER_00 (36:44):
Yes.
It sends an immediate browserpush notification to management.
It recommends an immediatespecific schedule adjustment
like enforcing a mandatoryweekend off.
And it flags human resources toensure compliance.
SPEAKER_01 (36:55):
So it forces a systemic structural intervention
before the employee snaps, handsin their badge, and walks out
the door.
SPEAKER_00 (37:03):
Precisely.
It replaces performativecorporate gestures with
actionable operationalcompassion.
SPEAKER_01 (37:09):
Actionable compassion.
I love that.
SPEAKER_00 (37:11):
Yeah.
Instead of management throwing acheap pizza party in the break
room for a team of completelyexhausted staff, the system
demands a real solution.
SPEAKER_01 (37:19):
It says this Pacific CNA has worked six weekends in a
row on the heaviest memory careunit.
They need Tuesday and Wednesdayoff immediately, and here is how
you will cover their shifts.
SPEAKER_00 (37:30):
Exactly.
And the data proves thisstructural empathy works.
Facilities that were earlyadopters of this wellness
scoring saw a 38% reduction inburnout-related incidents.
SPEAKER_01 (37:40):
38%, which brings all of these incredibly complex
threads together perfectly.
As we wrap up this deep dive,the overriding, undeniable
message from all of thesesources, from AHCA, from
Ziegler, from the senior CREwhite papers, is that the senior
living workforce crisis is notjust a recruiting problem.
SPEAKER_00 (37:57):
But it's really not.
SPEAKER_01 (37:58):
It is not a problem you can solve by just running
more ads on job boards oroffering a small sign-on bonus.
SPEAKER_00 (38:03):
It is a structural care model problem.
SPEAKER_01 (38:06):
Yeah.
SPEAKER_00 (38:06):
The foundation itself has cracked.
SPEAKER_01 (38:08):
Exactly.
The only way forward, the onlyway to fix the doom loop is this
synthesized, multi-prongedapproach.
You have to integrate ethical,predictive AI to stop the
financial bleeding of turnover.
SPEAKER_00 (38:21):
Yes.
SPEAKER_01 (38:21):
You have to undergo a massive operational redesign,
utilizing remote patientmonitoring and sensors to make
the physical job manageable.
You have to break open the siloand integrate community health
workers.
SPEAKER_00 (38:33):
Absolutely.
SPEAKER_01 (38:34):
And you must have a genuine data-backed commitment
to the financial value of humanwellness and management quality.
SPEAKER_00 (38:40):
If we connect all of this to the bigger picture for
you, the listener, whether youare managing a hundred-bed
facility, analyzing healthcareread investments, or you are
simply fascinated by the futureof work, the core lesson here is
dual-sided.
How so?
You cannot simply technologyyour way out of a structural
staffing problem by throwingsensors at it.
But equally, you cannot retainyour way out of a fundamentally
(39:03):
broken, exhausting care model byjust paying people more to
suffer.
You must attack both thetechnology and the care model
simultaneously.
SPEAKER_01 (39:13):
You have to fix the massive hole in the bathtub and
stop the leak at the exact sametime.
SPEAKER_00 (39:18):
Exactly.
SPEAKER_01 (39:18):
It is the only way the math and the human reality
ever align.
And, you know, exploring thesheer predictive power of this
technology leaves me with onefinal kind of wild provocative
thought to mull over as we closeout.
SPEAKER_00 (39:33):
What's that?
SPEAKER_01 (39:33):
Well, we've been talking entirely about senior
care today.
SPEAKER_00 (39:36):
Right.
SPEAKER_01 (39:36):
But if AI platforms can now accurately predict when
a healthcare worker is 90 daysaway from quitting and not only
predict it, but prescribe theexact schedule change or the
precise pay bump or the specificrest period needed to save them,
what happens when thistechnology scales to every other
industry?
SPEAKER_00 (39:52):
Oh well.
It's a profound question aboutthe future of the modern
workplace.
SPEAKER_01 (39:56):
Right.
What happens when it scales tologistics, to retail, to
corporate finance?
Are we entering an era where ourworkplace software will know
we're unhappy, burned out, andready to quit our jobs before we
even admit it to ourselves inthe mirror?
SPEAKER_00 (40:10):
That's a little scary.
SPEAKER_01 (40:12):
It is.
We are moving rapidly from aworld where business metrics
were simple, clean, and binaryto a world where the hidden,
deeply complex currents of humanbehavior are visible,
measurable, and highlypredictable.
SPEAKER_00 (40:24):
The muddy waters are clearing up, but what we see
underneath changes the nature ofwork entirely.
SPEAKER_01 (40:29):
It really does.
Thanks for joining us on thisdeep dive.
Imagine knowing with like 89% certainty that your
absolute best employee is gonnaquit their job.
SPEAKER_00 (00:06):
Right.
SPEAKER_01 (00:06):
And you know this uh a full month before they even
make the conscious decision todo it themselves.
Yeah.
SPEAKER_00 (00:13):
It's wild.
SPEAKER_01 (00:14):
Which is crazy.
You haven't read their personaltext messages, you haven't, you
know, listened in on their breakroom conversations, you haven't
violated their privacy in anyway whatsoever.
Instead, you've just beenquietly observing the uh the
invisible, completelyoperational footprint that they
leave behind in your dailyscheduling software.
SPEAKER_00 (00:33):
Aaron Powell I mean when you put it like that, it
sounds like science fiction.
SPEAKER_01 (00:36):
Trevor Burrus Yeah, a little bit.
SPEAKER_00 (00:37):
Or like perhaps uh corporate surveillance dystopia,
right?
Trevor Burrus, Jr.
But it's actually a reality.
It is fundamentally rewritingthe operational playbook for
really one of the most strainedindustries in our entire
economy.
SPEAKER_01 (00:50):
Aaron Powell Absolutely.
SPEAKER_00 (00:50):
Aaron Ross Powell We're talking about predictive
retention.
SPEAKER_01 (00:53):
Aaron Ross Powell Well, welcome to a brand new
deep dive.
Whether you're listening to thisas, say, a facility operator
who's frantically trying tofigure out how you're going to
cover a 3 a.m.
memory care shift.
SPEAKER_00 (01:05):
Aaron Powell We've all been there.
SPEAKER_01 (01:06):
Trevor Burrus Right.
Or maybe you're a concernedfamily member trying to track
the quality of care your lovedones are getting, or you know,
just a deeply curious learnerwho is fascinated by massive
systemic problem solving.
SPEAKER_00 (01:20):
You're in the right place.
SPEAKER_01 (01:21):
You are in the exact right place.
Because today we are looking ata stack of incredibly detailed,
frankly, eye-opening sources.
SPEAKER_00 (01:29):
Yeah, we've got a lot on the desk today.
SPEAKER_01 (01:30):
We do.
Sitting right here, we haveindustry reports from A.
Shann Cal, Ziegler, Argentum,and the National Investment
Center.
SPEAKER_00 (01:37):
Which are huge players.
SPEAKER_01 (01:38):
Massive.
And right next to those, we havethis series of incredibly
comprehensive white papers fromSenior C R E.
They detail this platform calledthe Workforce Retention
Intelligence Engine.
SPEAKER_00 (01:49):
Or uh WRIE for short.
SPEAKER_01 (01:51):
W-R-I-E, yeah.
And so the mission for this deepdive is to unpack this massive
looming labor crisis in seniorcare and assisted living.
SPEAKER_00 (02:01):
But we aren't just going to sit here and admire the
problem.
SPEAKER_01 (02:03):
No, definitely not.
We are exploring a completeparadigm shift today.
We are looking at how an entireindustry is attempting to move
away from this frantic,financially ruinous cycle of
constantly replacing burned-outstaff.
SPEAKER_00 (02:18):
Aaron Powell Exactly.
The data we're looking atdetails a shift toward using
proactive intelligence toprevent turnover before it ever
happens.
SPEAKER_01 (02:26):
Right.
SPEAKER_00 (02:27):
And in the process, completely redesigning the
physical and, well, theemotional nature of the work
itself.
SPEAKER_01 (02:32):
Aaron Powell Which is so needed.
And just to set the stage rightout of the gate for you
listening, we are exploring thistopic purely based on the data
provided in these sources.
SPEAKER_00 (02:41):
Aaron Powell Right, strictly the data.
SPEAKER_01 (02:42):
Aaron Powell Strictly the data.
We're analyzing how a vitalsector is attempting to
completely rewire itsoperational DNA just to survive.
We aren't here to, you know,debate the broader politics of
healthcare or labor.
SPEAKER_00 (02:54):
No, we're just following the numbers, the
technology, and the outcomes.
SPEAKER_01 (02:57):
Aaron Powell Exactly.
And before we can even begin tounderstand this AI-driven
solution, I mean we really haveto grasp the sheer scale of the
crisis first.
SPEAKER_00 (03:05):
Oh, absolutely.
SPEAKER_01 (03:06):
We have to look at the flawed mental models that
operators have been using tofight it.
So let's start with thedemographic cliff.
SPEAKER_00 (03:13):
Aaron Powell Yeah, the demographic cliff is like
the necessary foundation forthis entire conversation.
Because the map here is it'ssimply staggering.
SPEAKER_01 (03:23):
It really is.
SPEAKER_00 (03:24):
So according to the data from the National
Investment Center in Argentum,the senior living industry is
going to need 660,000 moreworkers by 2033.
SPEAKER_01 (03:35):
Wait, 660,000?
SPEAKER_00 (03:37):
Yeah.
And that is just to maintainbaseline operations.
SPEAKER_01 (03:40):
Just to keep the doors open and do what they're
doing right now.
SPEAKER_00 (03:43):
Exactly.
But when you zoom out to look atthe total new hires needed to
cover both industry growth andthe massive worker turnover.
SPEAKER_01 (03:52):
Because turnover is huge.
SPEAKER_00 (03:53):
Right.
The projections indicate a needfor over 1.3 million new hires
by 2030.
SPEAKER_01 (03:59):
Over a million new hires in just a few years.
SPEAKER_00 (04:02):
Yeah, let that sink in.
SPEAKER_01 (04:04):
I mean, let's think about the gravity of that number
for a second.
We are talking about attemptingto recruit a population roughly
the size of the entire city ofDallas into jobs that are
physically demanding,emotionally draining, and let's
be honest, historically lowpaying.
SPEAKER_00 (04:20):
Historically very low paying.
SPEAKER_01 (04:21):
And they have to do this while 63% of providers are
already facing intense staffingshortages right now.
Today.
SPEAKER_00 (04:28):
Right.
The sources note that 87% ofoperators are currently
reporting difficulty hiring.
87%.
SPEAKER_01 (04:36):
That's basically everybody.
SPEAKER_00 (04:37):
Pretty much.
Plus, the workforce itself isaging out.
A full 43% of the current staffare aged 50 and older.
SPEAKER_01 (04:45):
Wow.
So nearly half the workforce is50 plus.
SPEAKER_00 (04:49):
Exactly.
So you have a workforce that isnaturally aging out of the
physically demanding aspects ofthe job right at the precise
moment a massive generation ofseniors requires their care.
SPEAKER_01 (04:58):
It's the perfect storm.
SPEAKER_00 (05:00):
It really is.
And to understand why operatorsare failing to meet this moment,
we have to look at the turnoverrates.
SPEAKER_01 (05:05):
Oh, the turnover rates are brutal.
SPEAKER_00 (05:07):
We are talking about an industry-wide annual turnover
rate for frontline staff thatsits at an abysmal 50 to 80
percent.
SPEAKER_01 (05:15):
Wait, 80 percent, like annually.
SPEAKER_00 (05:17):
Annually.
A facility could literally bereplacing 80 percent of its
direct care staff every single12 months.
SPEAKER_01 (05:24):
That is insane.
It's like it's like trying tofill a bathtub with a massive
hole in the bottom.
SPEAKER_00 (05:29):
That's a great analogy.
SPEAKER_01 (05:30):
Right.
But instead of fixing the leak,operators are just spending all
their capital buying bigger andbigger buckets to pour water in.
SPEAKER_00 (05:37):
Yep.
Which brings us to the financialmechanics of this crisis.
Because usually, you know, whenwe talk about a business's
profit and loss statement, thereis this expectation of
precision.
SPEAKER_01 (05:47):
Sure, you want the spreadsheet to balance.
SPEAKER_00 (05:49):
Right.
You look at the labor line on aspreadsheet, the math shows a
specific number, and it feelsclean, paid or unpaid.
SPEAKER_01 (05:56):
But I'm guessing it's not that clean here.
SPEAKER_00 (05:58):
Aaron Powell No, we like financial realities to be
visible and neatly categorizedinto tidy little rows.
But in senior care, the truecost of labor is completely
hidden beneath the surface.
SPEAKER_01 (06:08):
How so?
SPEAKER_00 (06:09):
It is the absolute definition of financial muddy
waters.
Because when you ask an operatorwhat it actually costs, when a
certified nursing assistant, aCNA, leaves their facility, the
visible cost, the number theyactually track on that clean
spreadsheet, is usually statedas thirty five hundred to five
thousand dollars.
SPEAKER_01 (06:28):
Aaron Powell Okay.
So three and a half to fivegrand.
That makes sense on paper.
I mean, I assume that covers therecruitment ads, the background
check.
SPEAKER_00 (06:35):
Right, the onboarding paperwork.
SPEAKER_01 (06:36):
Maybe a few weeks of shadowing.
SPEAKER_00 (06:38):
Exactly.
Those are the direct costs.
SPEAKER_01 (06:40):
Right.
SPEAKER_00 (06:40):
But the senior CRE sources provide a really
comprehensive accounting of thetrue cost, the all-in
replacement cost.
SPEAKER_01 (06:48):
Aaron Powell And this is where the traditional
financial model completely fallsapart, right?
SPEAKER_00 (06:51):
Completely.
Yeah.
Because you have to factor inthe indirect costs.
When that CNA leaves, theirshifts don't just magically
disappear.
SPEAKER_01 (06:58):
The patients still need care.
SPEAKER_00 (07:00):
Exactly.
Yeah.
So the facility is forced to payyou forty to eighty percent
premium to a temporary agencyjust to bring in a stop cap
worker.
SPEAKER_01 (07:08):
Oh wow.
So they're paying almost doublejust to put a body in the
building, the bigger bucket forthe leaky bathtub.
SPEAKER_00 (07:13):
Precisely.
And it doesn't stop with agencypremiums either.
There is a massive productivityloss.
SPEAKER_01 (07:20):
Because the new person doesn't know the ropes.
SPEAKER_00 (07:22):
Exactly.
A brand new CNA, even anexperienced one who is just new
to this specific building,operates at only 60 to 70%
efficiency for their first 60 to90 days.
SPEAKER_01 (07:34):
That makes total sense.
They don't know the facilitylayout.
SPEAKER_00 (07:37):
They don't know which residents prefer their
medication crushed inapplesauce.
SPEAKER_01 (07:40):
Right.
Or they don't know the subtleinterpersonal dynamics of the
floor, like who gets along withwho.
SPEAKER_00 (07:45):
Exactly.
So everything takes longer.
Then you have the manager timediverted away from patient care
and into HR crisis management.
Trevor Burrus, Jr.
SPEAKER_01 (07:53):
Conducting interviews.
Yeah.
SPEAKER_00 (07:55):
Managing the chaotic schedule.
SPEAKER_01 (07:57):
Right.
And then there's the ultimatehidden cost mentioned in the
data, which just blew my mind.
Resident move out.
SPEAKER_00 (08:03):
Oh, this is massive.
SPEAKER_01 (08:05):
The sources note that resident dissatisfaction,
which is almost always driven byinconsistent care from a
rotating cast of stressed-outtemporary workers, can actually
result in a move out.
SPEAKER_00 (08:14):
Yeah, families notice when there's a new face
every week.
SPEAKER_01 (08:17):
And they don't like it.
And a single resident departurecan mean$40,000 to$80,000 in
lost revenue.
SPEAKER_00 (08:24):
It's a huge hit.
SPEAKER_01 (08:25):
So one frustrated nurse quitting could literally
trigger a chain reaction thatcosts a facility a year's worth
of profit.
SPEAKER_00 (08:32):
Which brings the true cost of a single CNA
turnover event into very starkrelief.
When you roll all of thoseindirect and direct costs
together, the real number isactually$8,000 to$15,000 per
CNA.
SPEAKER_01 (08:47):
Wow.
Up to$15,000.
SPEAKER_00 (08:49):
Yeah.
And for a registered nurse inRN, the true replacement cost is
$40,000 to$60,000.
SPEAKER_01 (08:55):
Let's let's actually do the math on that for a
standard 100-bed facility,because I think this is where it
gets real.
Let's do it.
Okay.
Let's say they have 50 directcare staff members.
If they have a 60% turnoverrate, which, you know, we said
is completely average for thisindustry, that means they are
replacing 30 employees everysingle year.
SPEAKER_00 (09:14):
30 people a year, yeah.
SPEAKER_01 (09:15):
Even if we use a super conservative$4,000 per
turnover and just direct costs,they are bleeding$120,000 a
year.
SPEAKER_00 (09:22):
Just indirect visible costs.
SPEAKER_01 (09:25):
Right.
But if we use the true all-incost of up to$15,000 per
departure, I mean, we aretalking about nearly half a
million dollars vanishing intothin air every single year.
SPEAKER_00 (09:37):
Just to maintain the exact same level of inadequate
staffing.
SPEAKER_01 (09:41):
It fundamentally changes the narrative around
wages, doesn't it?
SPEAKER_00 (09:44):
It really does.
SPEAKER_01 (09:45):
Wait, hold on.
This is where the logic seemscompletely broken to me.
If you talk to operators, theirimmediate default defense is
always, well, we simply cannotpay competitive wages because
our operating margins are tootight.
SPEAKER_00 (09:57):
You hear that all the time.
SPEAKER_01 (09:58):
Right.
But if we look at these numbers,aren't the margins incredibly
tight specifically becausethey're constantly bleeding half
a million dollars a yearreplacing people?
SPEAKER_00 (10:06):
Yes.
SPEAKER_01 (10:07):
Isn't the low wage strategy the exact thing
destroying their margins in thefirst place?
SPEAKER_00 (10:12):
That is the crucial realization the data points us
toward.
The industry is trapped in asystemic doom loop.
SPEAKER_01 (10:19):
A doom loop.
SPEAKER_00 (10:20):
Yeah.
Think about it.
People leave because the work isdemanding, the wages are low,
and they are burned out.
Their departure forces theoperator to hire agency staff at
an exorbitant 80% premium.
That agency spend shrinks theoperating margins even further.
SPEAKER_01 (10:38):
Because they're bleeding cash.
SPEAKER_00 (10:39):
Right.
And because the margins areshrunk, the operator genuinely
feels they cannot afford BATHERwage increases for their
permanent loyal staff.
SPEAKER_01 (10:48):
Oh man.
Which leads to more low wages,more burnout, and more people
quitting.
SPEAKER_00 (10:53):
It's a self-reinforcing cycle of
financial disaster.
SPEAKER_01 (10:56):
It really is.
And the core thesis embedded inthese senior CRE white papers is
that retention is no longer justlike a soft HR function.
SPEAKER_00 (11:04):
No, it's not about pizza parties or employee of the
month plaques anymore.
SPEAKER_01 (11:08):
Thank goodness.
Retention is the ultimatefinancial strategy.
Reducing turnover is the primarymechanism to actually fund the
wage increases that theworkforce is rightfully
demanding.
SPEAKER_00 (11:18):
You have to patch the leak to afford the water.
SPEAKER_01 (11:20):
Exactly.
So how exactly do you patch aleak before the pipe even
bursts?
Because human behavior isnotoriously unpredictable, or at
least we like to think it is.
SPEAKER_00 (11:29):
We do like to think that.
SPEAKER_01 (11:30):
This leads us to the massive conceptual shift
introduced by the WRIE platform,the workforce retention
intelligence engine.
This is an AI system designed toliterally predict when someone
is going to quit.
SPEAKER_00 (11:44):
It is.
The platform analyzes over 40distinct behavioral and
operational signals to predictturnover risk.
And it doesn't just guess.
You know, it models out risk on30, 60, and 90-day horizons.
SPEAKER_01 (11:57):
And the predictive accuracy is what initially
caught my eye in these reports.
The sources state that for a30-day departure risk, the
accuracy is 89%.
SPEAKER_00 (12:06):
89%.
It's incredibly high.
SPEAKER_01 (12:08):
That's almost certain.
SPEAKER_00 (12:09):
Yes.
And it achieves this by treatingthese horizons as escalating
intervention points.
Aaron Powell, Jr.
SPEAKER_01 (12:13):
What does that mean?
SPEAKER_00 (12:14):
So the 90-day risk acts as an early warning signal.
It allows for proactive cultureimprovements or maybe just
subtle check-ins from a manager.
SPEAKER_01 (12:21):
Like, hey, how are things going?
SPEAKER_00 (12:23):
Exactly.
Then the 60-day risk triggerselevated concern and maybe a
review of their upcomingschedule.
By the time someone hits the30-day risk threshold, the data
suggests the employee is showingstrong active departure
indicators.
SPEAKER_01 (12:37):
Aaron Powell Like they've already checked out
mentally.
SPEAKER_00 (12:39):
Right.
And at that point, immediatetargeted intervention is
required to save them.
SPEAKER_01 (12:44):
I have to admit, when I first read the phrase AI
analyzing my flight risk, mybrain immediately went to a very
dark, very dystopian place.
SPEAKER_00 (12:53):
Oh, of course it did.
SPEAKER_01 (12:54):
I pictured a manager tracking my bathroom breaks or
monitoring my personal textmessages or, you know, reading
my emails to see if I wassending a resume out.
How does a facility trackbehavior this closely without
completely destroying whateverfragile trust is left between
management and the staff?
SPEAKER_00 (13:12):
It's a vital question.
And the sources address thishead-on through what they term
privacy-first design and strictethical guardrails.
SPEAKER_01 (13:20):
Okay, good.
SPEAKER_00 (13:20):
The critical distinction here is the
difference between invasivesurveillance and supportive
operational intelligence.
SPEAKER_01 (13:26):
Okay, so what does that distinction actually look
like in the code?
How does it operate day-to-day?
SPEAKER_00 (13:31):
First off, W-R-I-E explicitly excludes all
protected classes from itsalgorithms.
It is completely blind to age,race, gender, and religion.
SPEAKER_01 (13:41):
Aaron Powell That's essential.
SPEAKER_00 (13:42):
Definitely.
But more importantly, itabsolutely forbids invasive
surveillance.
There is no keystroke loggingwhatsoever.
SPEAKER_01 (13:50):
Thank goodness.
SPEAKER_00 (13:51):
Right.
There is no monitoring ofpersonal devices or company
phones.
There is no social mediascraping, no reading of personal
emails, and absolutely no GPStracking outside of the physical
facility.
SPEAKER_01 (14:02):
Okay, so if it's not reading my text to see if I'm
angry about my boss, and it'snot tracking my location to see
if I'm at a job interview acrosstown, how on earth does it know
I'm going to quit with 89%accuracy?
SPEAKER_00 (14:14):
By analyzing the operational data that is already
naturally generated by thefacility's existing management
systems.
SPEAKER_01 (14:20):
What kind of data?
SPEAKER_00 (14:21):
It looks at the footprint an employee leaves in
the scheduling and payrollsoftware.
For example, it tracksscheduling patterns.
Has an employee suddenly startedrequesting an unusual number of
shift swaps?
SPEAKER_01 (14:32):
Oh, interesting.
SPEAKER_00 (14:33):
Right.
Are they calling out on Mondaysconsistently?
Have their schedule preferenceschanged rapidly?
Or very tellingly, has anemployee who historically always
accepted overtime shiftssuddenly started refusing them?
SPEAKER_01 (14:45):
Ah, okay.
So it's looking for a deviationfrom their personal baseline.
SPEAKER_00 (14:49):
Exactly.
SPEAKER_01 (14:49):
So if I usually jump at time and a half pay, but
suddenly I'm saying no to everysingle extra shift, the system
flags that my motivation or myenergy has fundamentally
shifted.
SPEAKER_00 (14:59):
That's exactly how it works.
It also ingests compensationsignals.
SPEAKER_01 (15:03):
Which means what?
Exactly.
SPEAKER_00 (15:04):
It looks at the time elapsed since their last raise,
and it compares their currentpay rate to real-time market
benchmarks.
SPEAKER_01 (15:12):
Oh, wow.
SPEAKER_00 (15:13):
Yeah.
So if a CNA hasn't had a raisein 18 months, and the system
knows that the fast foodrestaurant down the street just
raised their starting wage to$18an hour, the risk score
elevates.
SPEAKER_01 (15:24):
Because the system knows they could literally walk
across the street and make moremoney flipping burgers with way
less stress.
SPEAKER_00 (15:30):
Exactly.
And the outcome of thesepredictions is crucial to
understand.
The system is designed to bepurely supportive, never
punitive.
SPEAKER_01 (15:37):
That's a fine line to walk.
SPEAKER_00 (15:39):
It is, but the scores are only visible to
direct supervisors and HR.
The goal is to trigger anintervention that helps the
employee, not punishes them forbeing unhappy.
SPEAKER_01 (15:49):
Okay, so the system is gathering the scheduling and
compensation data ethically, andit's mapping out long-term
flight risk.
Right.
But let's bring this down to theground level.
How does this actually play outin reality on a random Tuesday
morning at 5 a.m.
when total chaos strikes andsomeone calls in sick?
SPEAKER_00 (16:06):
The dreaded 5 a.m.
call out.
SPEAKER_01 (16:08):
Exactly.
Knowing someone might quit in 90days is great, sure, but a
manager still has to staff thefloor in two hours.
SPEAKER_00 (16:15):
And this is where we see the transition from
long-term retention to immediateoperational triage.
The platform features apredictive call-out engine
designed specifically for thisexact scenario.
SPEAKER_01 (16:27):
A predictive call-out engine.
SPEAKER_00 (16:29):
Yeah.
It aims to forecast staffinggaps 24 to 72 hours before they
actually happen.
SPEAKER_01 (16:34):
Aaron Powell And the sources claim it does this with
72% accuracy.
That's that's wild.
It knows someone is going tocall out sick three days before
they even wake up with a sorethroat.
How is that mechanicallypossible?
SPEAKER_00 (16:44):
Aaron Powell The machine learning models ingest
18 distinct data signals,looking back over 18 months of
historical facility data.
SPEAKER_01 (16:51):
Aaron Powell Eighteen signals, like what?
SPEAKER_00 (16:53):
Well, some of the inputs are quite logical.
It pulls in weather forecasts,knowing that a predicted massive
snowstorm or a severe heat wavecorrelates with a spike in
absences.
SPEAKER_01 (17:03):
Okay.
Yes.
Snowstorms equal call-outs, thatmakes sense.
SPEAKER_00 (17:06):
Aaron Powell It also looks at local event calendars
and recurring historicalpatterns, what the industry
calls Monday spikes or holidayweekend drop-offs.
SPEAKER_01 (17:14):
Aaron Powell But it goes deeper than just knowing
it's gonna snow or that it's aholiday, right?
The white paper mentions thatthe AI models team dynamics and
notes that some call outs areactually contagious.
SPEAKER_00 (17:25):
Contagious call outs, yes.
SPEAKER_01 (17:26):
Aaron Powell This was a massive aha moment for me.
Does the system actually knowthat if Sarah calls out on
Monday, John is statisticallylikely to call out on Tuesday?
SPEAKER_00 (17:34):
Yes.
And understanding the mechanismbehind that is really
fascinating.
The machine learning algorithmis designed to identify hidden
historical patterns that a humanmanager who is, you know, bogged
down in daily crisis managementwould simply miss.
SPEAKER_01 (17:49):
Right.
They're too busy putting outfires.
SPEAKER_00 (17:51):
Exactly.
A human manager might be toobusy to realize that over the
last year, whenever the dementiaunit is short staffed by one
person, a specific second personalmost always calls out the very
next day.
SPEAKER_01 (18:04):
Because the physical workload of covering for that
missing person was so punishingthat the second person is just
physically and emotionallydestroyed by the end of their
shift.
SPEAKER_00 (18:12):
Yep.
SPEAKER_01 (18:13):
It's a domino effect of exhaustion.
SPEAKER_00 (18:15):
Precisely.
The AI maps these team cohesionscores and tracks high-risk
pairings.
It understands the operationalfriction between different SAV
members and different residentacuities.
SPEAKER_01 (18:26):
That is incredible.
SPEAKER_00 (18:27):
And think about the massive financial and
operational impact of knowingthis in advance.
Without this system, a managerfinds out about a call-out two
hours before a shift begins.
SPEAKER_01 (18:37):
And they panic.
SPEAKER_00 (18:38):
Total panic.
They pull out a binder, make adozen phone calls to staff who
do not want to answer theirphones at 5 a.m.
SPEAKER_01 (18:44):
I certainly wouldn't.
SPEAKER_00 (18:45):
Right.
And ultimately, they're forcedto hire a temp agency worker at
that 80% premium just tomaintain legal compliance on the
floor.
SPEAKER_01 (18:54):
They are forced to buy the bigger bucket again.
SPEAKER_00 (18:56):
But if the system predicts a high probability of a
gap 72 hours out, everythingchanges.
The system automaticallysurfaces coverage options.
SPEAKER_01 (19:05):
Automatically?
SPEAKER_00 (19:06):
Yes.
It can activate a standbyinternal slope pool at normal
base wages.
It can send targeted textmessages offering an open shift
specifically to staff memberswho the system knows are not at
risk of hitting overtime pay.
SPEAKER_01 (19:20):
Oh, that's smart.
So it avoids paying time and ahalf if it doesn't have to.
SPEAKER_00 (19:23):
Exactly.
It completely removes themanager from the frantic manual
phone tree process andintercepts the exorbitant agency
spend before the crisis evenmaterializes.
SPEAKER_01 (19:34):
Aaron Powell, which leads directly into the
platform's retention ROI engine.
Because, you know, predicting acall out saves money today, but
the facility still has to provethat these broader interventions
are actually fixing the doomloop over time.
SPEAKER_00 (19:46):
Aaron Powell Right.
You have to prove the long-termvalue.
SPEAKER_01 (19:48):
And this part of the platform tracks the literal
financial return on investmentfor retention efforts.
It benchmarks compensation usingreal-time data from the Bureau
of Labor Statistics and theAHCA.
SPEAKER_00 (19:58):
So an operator is never guessing if their wages
are competitive.
They can see exactly where theysit relative to the local
market.
SPEAKER_01 (20:05):
And it calculates that$8,000 to$15,000 turnover
cost we discussed earlier, butit models it out for every
specific role in that specificbuilding.
SPEAKER_00 (20:13):
It makes the invisible PL visible.
SPEAKER_01 (20:15):
Yes.
But it goes beyond justmeasuring the problem.
It triggers automated actionstoo.
For instance, it manages a peerrecognition and kudo system.
SPEAKER_00 (20:24):
I love this feature.
SPEAKER_01 (20:25):
Me too.
Staff can award points to eachother for helping out with a
difficult lift or, you know,covering a break.
And the data shows that thisspecific type of peer-to-peer
recognition directly reduces90-day attrition rates.
SPEAKER_00 (20:38):
Because feeling valued by your peers is
incredibly powerful.
SPEAKER_01 (20:42):
Definitely.
And the overall result ofoperators utilizing this kind of
predictive scheduling and ROItracking, a 23% annual turnover
reduction.
SPEAKER_00 (20:52):
That is a massive operational victory.
23% is huge.
SPEAKER_01 (20:56):
It really is.
SPEAKER_00 (20:56):
It is significant.
But I think we have to look atthe broader reality of the job
here.
Predicting when staff will leaveor perfectly optimizing the
schedule, that's only one pieceof a very complex puzzle.
True.
Because if the physical jobitself remains fundamentally
impossible or just incrediblyinefficient in grueling,
(21:16):
employees are eventually goingto leave no matter how
seamlessly you schedule theirshifts.
SPEAKER_01 (21:21):
Right.
You cannot retain people if thephysical environment they work
in is a nightmare.
SPEAKER_00 (21:26):
Exactly.
SPEAKER_01 (21:26):
Which brings us to a massive shift in how the work is
actually performed.
How does technology change thephysical daily reality of
caregiving?
SPEAKER_00 (21:36):
Well, this represents a vital evolution
from a reactive observationmodel to continuous passive
surveillance of the residents.
SPEAKER_01 (21:41):
Reactive observation meaning what?
SPEAKER_00 (21:43):
Traditionally, a CNA operates on a model of physical
rounds.
Every two hours, they walk downa long hallway, they open doors,
they knock, they peek in tocheck on residents, and then
they mark a chart.
SPEAKER_01 (21:54):
So instead of a security guard patrolling an
empty building every two hours,just hoping they happen to spot
something wrong at the exactExact moment they walk by.
What is the technologicalalternative?
SPEAKER_00 (22:03):
Aaron Powell The alternative is the remote
patient monitoring and sensorlayer, RPM.
Instead of relying purely onhourly physical rounds, the
facility installs passivesensors.
SPEAKER_01 (22:13):
Aaron Powell And let's clarify what we mean by
sensors here.
SPEAKER_00 (22:16):
Yes.
Very important.
We are not talking aboutinvasive cameras in bedrooms or
bathrooms.
SPEAKER_01 (22:20):
No one wants that.
SPEAKER_00 (22:21):
No.
And we aren't talking aboutwearable pendants or bracelets
that residents frequently takeoff, lose, or forget to charge.
SPEAKER_01 (22:29):
We are talking about ambient intelligence built into
the room itself floor vibrationsensors, bed sensors, room-based
radar systems.
SPEAKER_00 (22:36):
Correct.
And the capabilities outlined inthe white papers are just
remarkable.
Take the bed sensors thatmonitor sleep quality, for
example.
Okay.
By continuously analyzing sleeppatterns, the system can
actually spot the onset of aurinary tract infection, a UTI,
or a cardiac event 48 to 72hours early.
SPEAKER_01 (22:54):
Okay, stop right there.
How does a sensor under amattress or a vibration sensor
on the floor know that anelderly resident has a bacterial
infection?
That sounds like magic.
SPEAKER_00 (23:07):
It really does, but it comes down to establishing a
baseline of normal behavior andthen mathematically analyzing
subtle behavioral deviationsfrom that baseline.
SPEAKER_01 (23:16):
Okay, walk me through that.
SPEAKER_00 (23:17):
So a urinary tract infection in an elderly patient
doesn't always presentimmediately with a fever or
pain.
It often presents with subtlebehavioral changes.
SPEAKER_01 (23:26):
Like what?
SPEAKER_00 (23:26):
The floor sensors might detect a 30% increase in
bathroom visit frequency duringthe night.
SPEAKER_01 (23:31):
Oh, because they're getting up more often.
SPEAKER_00 (23:33):
Right.
And the bed sensor detects anincrease in restlessness,
tossing and turning, and subtlechanges in resting respiration
rates.
SPEAKER_01 (23:39):
So the system aggregates all those tiny
behavioral clues, the frequenttrips to the bathroom, the poor
sleep, the heavier breathing.
SPEAKER_00 (23:46):
Exactly.
By passively tracking thosemetrics, the algorithm flags an
anomaly days before severeclinical symptoms, like a high
fever, a dangerous fall due toweakness, or severe confusion
actually manifest.
SPEAKER_01 (23:59):
That is amazing.
SPEAKER_00 (24:00):
And there is gate analysis as well.
SPEAKER_01 (24:02):
Gate analysis.
SPEAKER_00 (24:03):
Yeah, the floor sensors can analyze the physical
footfalls of a resident.
Wow.
If a resident's normal walkingspeed subtly slows down over a
week, or if they start draggingtheir left foot slightly, the
system flags an elevated fallrisk before the resident ever
actually trips.
SPEAKER_01 (24:22):
And this technological layer completely
upends the traditional staffingmath, doesn't it?
SPEAKER_00 (24:27):
It really does.
SPEAKER_01 (24:28):
Because if you have this ambient AI constantly
monitoring the rooms for falls,wandering, or emerging
infections, you eliminate theneed for those blanket hourly
manual checks for low-riskresidents.
SPEAKER_00 (24:40):
Exactly.
Which allows the facility tomove to what the sources call
acuity-based staffing.
SPEAKER_01 (24:45):
Let's break down the economics of acuity-based
staffing.
SPEAKER_00 (24:47):
Okay.
So the data illustrates that ona traditional manual memory care
unit, a facility might need aCNA ratio of one caregiver to
every six residents just tosafely monitor everyone and
prevent wandering.
SPEAKER_01 (24:59):
One to six.
SPEAKER_00 (24:59):
But when you blanket that unit with well-implemented
remote patient monitoring, thatratio can safely shift from one
to six up to one to nine.
(25:23):
Huge savings.
SPEAKER_01 (25:24):
And they achieve that financial savings without
compromising safety.
SPEAKER_00 (25:28):
In fact, the data argues that safety actually
improves.
SPEAKER_01 (25:31):
Really?
How so?
SPEAKER_00 (25:33):
Because the staff isn't wasting time checking on
sleeping healthy residents,they're available immediately
when something actually happens.
The average critical alertresponse time under this
sensor-driven system drops tojust 4.2 minutes.
SPEAKER_01 (25:45):
That's incredibly fast, but there is a massive
warning sign flashing in thesesources regarding human behavior
and technology.
The concept of alert fatigue.
SPEAKER_00 (25:54):
Oh, yes.
Alert fatigue is real.
SPEAKER_01 (25:56):
Because just like a car alarm that goes off every
time the wind blows, if thesystem isn't perfectly
calibrated, it becomescompletely useless.
SPEAKER_00 (26:03):
Exactly.
If a bed sensor goes off everysingle time a resident merely
shifts their weight, the nursingstaff will be bombarded with
hundreds of false positivealarms a night.
SPEAKER_01 (26:12):
Which would drive anyone crazy.
SPEAKER_00 (26:14):
Very quickly, human nature takes over.
They will simply start ignoringthe dashboard, or worse,
unplugging the system entirely.
SPEAKER_01 (26:21):
So the technology completely fails if it is
deployed purely as a rigidsurveillance tool.
Right.
If management uses it to punishstaff for missing an alert
rather than integrating it intoa redesigned workflow that
genuinely assists them, thestaff will just reject it.
SPEAKER_00 (26:37):
The goal isn't automation for the sake of
replacing humans.
The goal is the precisiondeployment of human attention.
SPEAKER_01 (26:44):
I like that phrase.
Precision deployment.
SPEAKER_00 (26:47):
The technology has to be individually calibrated to
each residence-specific baselineso that when an alarm rings, the
staff knows it is a genuine,actionable emergency.
SPEAKER_01 (26:58):
So we've put sensors in the rooms to stop the
physical exhaustion of walkingthe halls all night.
The technology makes thephysical work more efficient and
targeted.
Yes.
But what about the emotionalexhaustion?
Caregiving is deeply inherentlyemotional work.
A floor sensor doesn't make acaregiver feel like their job
actually matters.
SPEAKER_00 (27:13):
No, it doesn't.
SPEAKER_01 (27:14):
To retain people in a notoriously tough field, you
have to connect their dailyrepetitive tasks to a larger
sense of purpose and actualclinical outcomes.
This requires a completelydifferent operational shift.
SPEAKER_00 (27:28):
It is a profound conceptual shift in how we view
the physical building itself.
SPEAKER_01 (27:33):
How so?
SPEAKER_00 (27:34):
Well, traditionally, a senior care facility operates
as a closed, isolated silo.
The facility hires all thestaff, they provide 100% of the
care, and they attempt to manageevery single aspect of a
resident's life within their ownfour walls.
SPEAKER_01 (27:48):
But the WRIE platform advocates for
shattering that silo entirely.
It pushes for what they call acommunity integrated hub.
SPEAKER_00 (27:56):
A community integrated hub.
SPEAKER_01 (27:57):
That's Kin X employee retention directly to
clinical outcomes.
I want to dive into thepsychology of this for a second.
How does showing a frontline CNAa data dashboard actually change
their job satisfaction?
SPEAKER_00 (28:08):
Aaron Powell Because it elevates the nature of the
work from mere task completionto profound clinical purpose.
SPEAKER_01 (28:13):
Give me an example.
SPEAKER_00 (28:14):
So the platform features an outcomes dashboard
that tracks macrometrics likehospital readmission rates and
successful ER diversions.
Imagine you are a CNA.
Your job is incredibly hard.
Yesterday, you spent an hourpainstakingly coaxing a resident
with dementia to drink twoglasses of water because you
noticed they were lethargic.
SPEAKER_01 (28:34):
Which is not easy to do.
SPEAKER_00 (28:35):
Not at all.
But today, you look at a screenand see that your specific
attention to their hydrationdirectly resulted in a
successful hospital diversion.
SPEAKER_01 (28:44):
Oh wow.
So you see the direct linebetween your hard work and a
human being staying out of theemergency room.
SPEAKER_00 (28:50):
Precisely.
The data shows that when staffcan visualize their measurable
clinical impact, they staylonger.
This connection alone yields a31% reduction in hospital
readmission.
SPEAKER_01 (29:00):
31%.
SPEAKER_00 (29:01):
Yeah.
It proves to the workforce thatthey are clinicians, not just
task rabbits.
SPEAKER_01 (29:05):
But to achieve those outcomes without burning out the
already exhausted clinicalnurses, the platform introduces
the strategic use of acompletely different role.
Community health workers orCHWs?
SPEAKER_00 (29:16):
CHWs are an incredibly underutilized asset
in senior care.
SPEAKER_01 (29:20):
And the economics of their role are vital here,
right?
SPEAKER_00 (29:23):
Absolutely.
They operate at a wage point ofroughly$18 to$26 an hour.
Compare that to clinical RNs whomake anywhere from$28 to$35 an
hour, or much more if they areagency temps.
SPEAKER_01 (29:35):
So what exactly is a CHW doing to offload the
pressure?
The white paper details a CHWtask queue and something called
SDOH domain screenings.
SPEAKER_00 (29:46):
Right.
So STOH stands for SocialDeterminants of Health.
SPEAKER_01 (29:49):
Okay.
SPEAKER_00 (29:50):
These are the crucial non-medical factors that
deeply impact a resident'soverall health things like
social isolation, foodinsecurity, transportation
barriers, and physical safety intheir environment.
SPEAKER_01 (30:00):
The day-to-day living stuff.
SPEAKER_00 (30:02):
Exactly.
The WRIE system systematicallytracks screenings across these
domains.
By having dedicated CHWs handlethese vital social and
navigational tasks, it frees upthe highly paid, highly stressed
clinical nurses to focusexclusively on what only they
can do clinical nursing work.
SPEAKER_01 (30:19):
It's a brilliantly simple concept when you think
about it.
You are expanding the capacityand the reach of the care team
without massively inflating thenursing payroll.
And this community integrationgoes even further.
The sources detail integratingPACE partnerships and telehealth
into the core infrastructure.
SPEAKER_00 (30:36):
PACE stands for Programs of All-Inclusive Care
for the Elderly.
SPEAKER_01 (30:40):
Okay.
SPEAKER_00 (30:40):
By integrating PACE partnerships, a facility can
actually bring external,federally, or state funded care
staff right into the building tosupport residents.
SPEAKER_01 (30:50):
Oh, that's smart.
Use outside resources.
SPEAKER_00 (30:52):
Yes.
And treating telehealth as core,embedded infrastructure is
another game changer.
Imagine an overnight shift.
A resident spikes a fever.
SPEAKER_01 (31:02):
Usually you'd have to call someone in.
SPEAKER_00 (31:03):
Right.
Instead of waking up an on-callnurse who has to physically
drive to the facility at 2 a.m.,the overnight staff has 247
access to a virtual telehealthnurse.
They get immediate clinicaldecision support.
SPEAKER_01 (31:15):
And I noticed the sources heavily emphasize
changing the relationship withthe residents' families too.
SPEAKER_00 (31:20):
Oh, this is a big one.
SPEAKER_01 (31:21):
They advocate for treating family members as
trained care partners, providingthem with structured
orientations on how to activelysupport care plans during their
visits.
SPEAKER_00 (31:30):
Which makes a lot of sense.
SPEAKER_01 (31:31):
It does.
But it's a huge departure fromtreating families merely as
customers who only get a phonecall when something goes wrong
or a bill is due.
It really transforms thefacility into a community hub.
SPEAKER_00 (31:43):
It connects all the disparate dots.
When the heavy burden of care isstructurally shared among CHWs,
integrated external PACEpartners, on-demand telehealth
nurses, and engaged familymembers, the direct care staff
are far less likely to becrushed by the workload.
SPEAKER_01 (32:01):
So we have optimized the schedule using predictive
AI.
We have upgraded the physicalreality of the building with
floor sensors and remotemonitoring.
And we have connected the workto a larger clinical purpose
while bringing in communitysupport.
SPEAKER_00 (32:14):
Right.
We've done a lot.
SPEAKER_01 (32:15):
But let's be entirely real for a moment.
None of that matters.
Not the sensors, not thetelehealth, not the perfect
schedule.
If your direct boss is terrible.
SPEAKER_00 (32:23):
No, it doesn't.
SPEAKER_01 (32:24):
Or if you are physically exhausted to the
point of total collapse.
SPEAKER_00 (32:27):
There is a very old, very universally accepted truth
in human resources.
People leave managers, notcompanies.
SPEAKER_01 (32:34):
Aaron Powell It's a cliche because it's true.
SPEAKER_00 (32:36):
Exactly.
You can have the best technologyin the world, but a toxic
supervisor will still drive yourbest talent out the door.
The final phases of thisplatform operationalize that
truth by attempting to measuremanagement quality objectively.
SPEAKER_01 (32:50):
Which is hard to do.
But the sources outline a deeplyintegrated 360-degree feedback
system.
It allows for continuous,anonymous peer, subordinate, and
supervisor evaluations, and ittracks those trends over time.
SPEAKER_00 (33:04):
And it even includes a feature called family praise
routing.
SPEAKER_01 (33:07):
Family praise routing.
Tell me about that.
SPEAKER_00 (33:10):
It's a subtle but powerful cultural lever.
Usually, when a family member ishappy with the care their mother
received, they might tell theexecutive director or maybe drop
a generic thank you card at thefront desk.
SPEAKER_01 (33:21):
Yeah, that's pretty typical.
SPEAKER_00 (33:22):
The system changes that.
When positive feedback issubmitted, it doesn't go to a
generic inbox.
The system routes it directly tothat specific staff member's
supervisor and logs it in theirpermanent personnel file.
SPEAKER_01 (33:33):
Oh wow.
SPEAKER_00 (33:34):
It forces a systemic culture of recognition.
The platform then generates asupervisor effectiveness score
based on all of these inputs.
SPEAKER_01 (33:42):
And the results of measuring management quality are
undeniably clear.
The data notes that facilitieswith top cordile managers see
89% staff satisfaction.
SPEAKER_00 (33:52):
89%.
SPEAKER_01 (33:53):
To achieve 89% satisfaction in an industry
currently experiencing 80%turnover is nothing short of
miraculous.
SPEAKER_00 (34:01):
It proves mathematically that management
quality compounds.
Better supervisors retain betterstaff.
Better staff who know theresidents and aren't burned out
deliver better care.
Better care leads to fewerhospitalizations and higher
revenue.
SPEAKER_01 (34:15):
But then we reach the final layer, staff wellness.
And I have to admit, when Ifirst read this section, my
skepticism spiked.
SPEAKER_00 (34:22):
I'm not surprised.
SPEAKER_01 (34:23):
The white paper details the use of AWS real-time
burnout alerts and tracks acomposite score from zero to a
hundred to literally measure anemployee's fatigue.
Right.
Can an algorithm actuallymeasure something as deeply
personal, subjective, andemotionally complex as burnout?
Burnout is a feeling, it's astate of mind.
How on earth does a computerknow I'm burnt out before I even
(34:44):
realize it myself?
SPEAKER_00 (34:45):
It is a completely natural reaction to question the
mechanization of human emotion.
But the data here asks us tocompletely reframe how we view
burnout.
SPEAKER_01 (34:56):
Okay, reframe it how.
SPEAKER_00 (34:57):
It demands that we stop treating burnout as a
character flaw or a weakness orjust a passing bad mood.
It treats burnout as aphysiological clinical condition
that is driven by very specific,measurable operational data
points.
SPEAKER_01 (35:11):
So what exactly is this algorithm measuring to
generate that zero to a hundredfatigue score?
SPEAKER_00 (35:16):
Well, it tracks the raw physical toll of the
schedule.
How many consecutive shifts hasthis CNA worked?
It monitors overtime hoursmathematically.
Okay.
It analyzes shift patterns toidentify known fatigue
thresholds, like someone workinga night shift, having eight
hours off, and then working anevening shift.
SPEAKER_01 (35:33):
That's a brutal turnaround.
SPEAKER_00 (35:34):
But most impressively, it measures a
staff member's exposure to highacuity residents.
SPEAKER_01 (35:38):
Explain that acuity exposure.
How does it track that?
SPEAKER_00 (35:41):
The system knows the clinical difficulty of every
resident in the building.
It knows who requires atwo-person lift to get out of
bed.
It knows who exhibits combativebehaviors due to advanced
dementia.
Right?
The algorithm calculates howmany days in a row a specific
employee has been assigned tothe most physically and
emotionally demanding patientsin the building.
SPEAKER_01 (36:01):
So it is quantifying the actual physical and
emotional load placed on thehuman body and mind.
SPEAKER_00 (36:07):
Exactly.
SPEAKER_01 (36:08):
What happens when the math decides that load is
too heavy, when that zero to ahundred score gets too high?
SPEAKER_00 (36:14):
It triggers tiered systemic interventions.
If a staff member score crosses60, it's flagged as high risk.
SPEAKER_01 (36:21):
And what does high risk do?
SPEAKER_00 (36:23):
The system automatically sends a
notification to the departmentsupervisor, triggering a
mandatory workload review.
The supervisor is required tolook at the next week's schedule
and adjust the acuity load.
SPEAKER_01 (36:33):
And if the score goes over 80.
SPEAKER_00 (36:35):
A score over 80 is classified as critical risk.
At this point, the systemdoesn't just suggest an
intervention, it demands one.
SPEAKER_01 (36:43):
Demands one.
SPEAKER_00 (36:44):
Yes.
It sends an immediate browserpush notification to management.
It recommends an immediatespecific schedule adjustment
like enforcing a mandatoryweekend off.
And it flags human resources toensure compliance.
SPEAKER_01 (36:55):
So it forces a systemic structural intervention
before the employee snaps, handsin their badge, and walks out
the door.
SPEAKER_00 (37:03):
Precisely.
It replaces performativecorporate gestures with
actionable operationalcompassion.
SPEAKER_01 (37:09):
Actionable compassion.
I love that.
SPEAKER_00 (37:11):
Yeah.
Instead of management throwing acheap pizza party in the break
room for a team of completelyexhausted staff, the system
demands a real solution.
SPEAKER_01 (37:19):
It says this Pacific CNA has worked six weekends in a
row on the heaviest memory careunit.
They need Tuesday and Wednesdayoff immediately, and here is how
you will cover their shifts.
SPEAKER_00 (37:30):
Exactly.
And the data proves thisstructural empathy works.
Facilities that were earlyadopters of this wellness
scoring saw a 38% reduction inburnout-related incidents.
SPEAKER_01 (37:40):
38%, which brings all of these incredibly complex
threads together perfectly.
As we wrap up this deep dive,the overriding, undeniable
message from all of thesesources, from AHCA, from
Ziegler, from the senior CREwhite papers, is that the senior
living workforce crisis is notjust a recruiting problem.
SPEAKER_00 (37:57):
But it's really not.
SPEAKER_01 (37:58):
It is not a problem you can solve by just running
more ads on job boards oroffering a small sign-on bonus.
SPEAKER_00 (38:03):
It is a structural care model problem.
SPEAKER_01 (38:06):
Yeah.
SPEAKER_00 (38:06):
The foundation itself has cracked.
SPEAKER_01 (38:08):
Exactly.
The only way forward, the onlyway to fix the doom loop is this
synthesized, multi-prongedapproach.
You have to integrate ethical,predictive AI to stop the
financial bleeding of turnover.
SPEAKER_00 (38:21):
Yes.
SPEAKER_01 (38:21):
You have to undergo a massive operational redesign,
utilizing remote patientmonitoring and sensors to make
the physical job manageable.
You have to break open the siloand integrate community health
workers.
SPEAKER_00 (38:33):
Absolutely.
SPEAKER_01 (38:34):
And you must have a genuine data-backed commitment
to the financial value of humanwellness and management quality.
SPEAKER_00 (38:40):
If we connect all of this to the bigger picture for
you, the listener, whether youare managing a hundred-bed
facility, analyzing healthcareread investments, or you are
simply fascinated by the futureof work, the core lesson here is
dual-sided.
How so?
You cannot simply technologyyour way out of a structural
staffing problem by throwingsensors at it.
But equally, you cannot retainyour way out of a fundamentally
(39:03):
broken, exhausting care model byjust paying people more to
suffer.
You must attack both thetechnology and the care model
simultaneously.
SPEAKER_01 (39:13):
You have to fix the massive hole in the bathtub and
stop the leak at the exact sametime.
SPEAKER_00 (39:18):
Exactly.
SPEAKER_01 (39:18):
It is the only way the math and the human reality
ever align.
And, you know, exploring thesheer predictive power of this
technology leaves me with onefinal kind of wild provocative
thought to mull over as we closeout.
SPEAKER_00 (39:33):
What's that?
SPEAKER_01 (39:33):
Well, we've been talking entirely about senior
care today.
SPEAKER_00 (39:36):
Right.
SPEAKER_01 (39:36):
But if AI platforms can now accurately predict when
a healthcare worker is 90 daysaway from quitting and not only
predict it, but prescribe theexact schedule change or the
precise pay bump or the specificrest period needed to save them,
what happens when thistechnology scales to every other
industry?
SPEAKER_00 (39:52):
Oh well.
It's a profound question aboutthe future of the modern
workplace.
SPEAKER_01 (39:56):
Right.
What happens when it scales tologistics, to retail, to
corporate finance?
Are we entering an era where ourworkplace software will know
we're unhappy, burned out, andready to quit our jobs before we
even admit it to ourselves inthe mirror?
SPEAKER_00 (40:10):
That's a little scary.
SPEAKER_01 (40:12):
It is.
We are moving rapidly from aworld where business metrics
were simple, clean, and binaryto a world where the hidden,
deeply complex currents of humanbehavior are visible,
measurable, and highlypredictable.
SPEAKER_00 (40:24):
The muddy waters are clearing up, but what we see
underneath changes the nature ofwork entirely.
SPEAKER_01 (40:29):
It really does.
Thanks for joining us on thisdeep dive.
Popular Podcasts
Betrayal Weekly
Betrayal Weekly is back for a new season. Every Thursday, Betrayal Weekly shares first-hand accounts of broken trust, shocking deceptions, and the trail of destruction they leave behind. Hosted by Andrea Gunning, this weekly ongoing series digs into real-life stories of betrayal and the aftermath. From stories of double lives to dark discoveries, these are cautionary tales and accounts of resilience against all odds. From the producers of the critically acclaimed Betrayal series, Betrayal Weekly drops new episodes every Thursday. If you would like to share your story, you can reach out to the Betrayal Team by emailing them at betrayalpod@gmail.com and follow us on Instagram at @betrayalpod and @glasspodcasts. Please join our Substack for additional exclusive content, curated book recommendations, and community discussions. Sign up FREE by clicking this link Beyond Betrayal Substack. Join our community dedicated to truth, resilience, and healing. Your voice matters! Be a part of our Betrayal journey on Substack.
Stuff You Should Know
If you've ever wanted to know about champagne, satanism, the Stonewall Uprising, chaos theory, LSD, El Nino, true crime and Rosa Parks, then look no further. Josh and Chuck have you covered.
Dateline NBC
Current and classic episodes, featuring compelling true-crime mysteries, powerful documentaries and in-depth investigations. Follow now to get the latest episodes of Dateline NBC completely free, or subscribe to Dateline Premium for ad-free listening and exclusive bonus content: DatelinePremium.com