May 21, 2025 • 20 mins
In this NextGen Healthcare Summit episode with Steve Santangelo of Garner Health, he delves into healthcare costs heading toward an unsustainable threshold where nearly 9% of company revenue will go toward benefits packages—the same percentage that contributed to GM's financial crisis in 2008. Data analysis reveals that which individual doctor a patient sees has the greatest impact on healthcare costs, with significant variations in quality and expense that don't necessarily correlate with each other.
• Top-performing doctors maintain complication rates around 5% for major surgeries while bottom-performing doctors approach 20%
• Approximately one-third of all healthcare procedures are deemed medically unnecessary
• Basic office visits cost around $100 with top doctors versus $250 with lower performers
• Traditional methods of evaluating doctor performance are highly inaccurate
• More granular assessment of doctor performance includes factors like tendency to jump to surgery, techniques used, complication rates, and setting choices
• Employers can save approximately 27% by directing employees to higher-quality providers
• Current provider directories are only about 27% accurate, creating barriers to finding good doctors
• AI and machine learning can help clean provider directories and deliver real-time information
• Using incentives to encourage patients to see high-quality doctors creates better outcomes while reducing costs
• Implementing these approaches can reduce employer healthcare spend by approximately 12%
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:01):
You're listening to Risk and Resolve.
And now for your hosts, benConner and Todd Hufford.
I'm Steve Santangelo.
As mentioned, I got in latelast night from New Jersey where
I live.
I do have a toddler and I'vedone the cookie battle many a
time, so I can resonate withthat.
But basically last night got tomy hotel, was thinking about
healthcare, which is reallywhere I'm focused.
(00:22):
I know Ben showed that awesomevideo highlighting kind of some
of the struggles that we'reseeing in healthcare today and
then thinking about AItechnology in the future and
kind of how to bring that alltogether in my presentation here
today.
And so what did I do in my hotelroom late last night?
And I went to chat GPT and Isaid how do you give a good
presentation?
And so there's basically twothings you got to do.
The first is introduce yourselfand you made me sound way more
(00:45):
important than I am.
And then two, you start withsomething thought provoking,
maybe a question or somethinglike that.
So for the audience, going backto 2008, huge financial crisis,
gm at the forefront of that.
Does anyone know what reallycaused that for GM?
What was the real impact totheir business that caused that
massive financial pressure?
The real impact to theirbusiness that caused that
(01:06):
massive financial pressure,didn't say if no one answers
what to do, so I'm going to goforward.
Anyway, basically this isreally the problem.
So GM got to a point wheretheir revenue percent of their
revenue 9% was attributed totheir pensions and benefit
offerings.
So think about that 9% goingjust towards that.
So that basically led WarrenBuffett to come out and
(01:27):
basically say GM's really not abusiness.
What they are is a huge annuity, an insurance company with
basically a major auto companyattached, and so that is pretty
powerful to see there.
Warren Buffett has also beenquoted as saying basically the
American healthcare system iskind of a tapeworm on the
American economy.
It gets more expensive everyyear.
You kind of saw that in Ben'svideo earlier.
(01:48):
It's just costs are continuingto go up and unfortunately
incentives are very misalignedacross the entire industry to
really impact that.
And so what's going on in theworld and again, I've been in
healthcare now over 15 yearswe're kind of all headed in that
direction.
That GM was, and so latestestimates basically say that by
2031, all employers in theentire United States are
(02:12):
basically going to be at orexceed that 9% of revenue going
towards benefit packages.
So we're kind of headed on acrash course.
We've been on that journey forquite some time.
Ben can speak veryintelligently to that, but when
I started at UnitedHealthcareover 15 years ago, we always
said if a premium got to $500,the whole system's going to shut
(02:34):
down.
Many of you in here areprobably paying double, if not
triple, that, depending upon ifyou're enrolled with families
and things of that nature.
So it's really an unsustainablecourse and unfortunately we got
to start to think differentlyif we're going to make an impact
on that.
And that's where I think all ofthese great things data
technology, ai can really helpus kind of tackle that problem.
(02:55):
So the thing that we've donehere at Garner is basically
looked at over 75% of all of themedical claims in the entire
country and that's a big dataset.
That requires a lot oftechnology machine readable
files, ai, things of that natureand what we said is well, what
is it that really impacts, costthe most?
(03:16):
And so what we found by usingthe data is really there's one
thing and it's really whichindividual doctor a patient sees
has the greatest impact on thecost of care, and if we can get
a lot of people to start seeingreally high quality doctors, we
can actually reverse that courseand start to bring down the
expected trend of healthcare.
(03:37):
And so what we've done iswritten over basically 500
different metrics, both qualityand cost focused.
But just wanted to highlightsome of the things that we see
in the data around.
First, complication rates frommajor surgery.
If you look at the top 25% ofdoctors, they average about a 5%
or better complication rate.
(03:58):
If you look at the bottom 25%of providers, you have almost a
20% complication rate, and again, that's just after a major
surgery.
But that applies basically toany healthcare needs you could
ever have.
And then the other thing thatwe're hearing out there.
A lot of people have talkedabout fraud, waste, abuse in the
healthcare system.
The reality is is that about athird of all healthcare out
(04:19):
there is deemed unnecessary,meaning it's a procedure, it's a
test, it's a drug, it'swhatever.
That should have never actuallyhappened.
And imagine that if we couldeliminate 30% of all healthcare
spend in the country againreally starting to bring down
that cost of care and sohighlighting kind of
inappropriate replacements ofjoints again unnecessary care
you get people to the top 25% ofdoctors.
(04:42):
That's sub 10%, unfortunately,if you end up seeing a bottom
25% provider, that's near 40%.
And so that's it from kind of aquality perspective.
There's also huge costimplications.
Obviously, a surgery that'sunnecessary is a huge cost
burden.
But you can see here, you know,just the basic cost of an
(05:02):
office visit.
So you get people to the top25% of doctors somewhere around
100 bucks.
Bottom 25, you're approachingto $250.
And then if you think aboutcosts of knee replacements,
there's, you know, huge costvariations just on the
negotiated rate between thepayers and the providers and you
see a staggering differencethere.
Top 25% of doctors you know inthe ballpark of $13,000, almost
(05:27):
$25,000 if you go to the bottomperforming doctors.
So what I wanted to do andhopefully these are no one's
relatives here, but we're gonnastart naming names and talking
about kind of what's been donein the world as it relates to
identifying high qualityperformers and kind of how
Garner's taking a differentapproach using the technology
(05:48):
and some of that machinelearning, and so historically
all doctors have been scored onthis thing called episode
groupers.
You basically take a wholebunch of patients attributed to
that doctor, you divide up theaverage cost per patient for a
specific set of codes, whateverthe condition might be, and you
basically get a total cost ofcare.
Again, I think we're allaligned here that having lower
(06:10):
cost is a good outcome, and soin that traditional approach,
you get one doctor who averages$13,000.
You have another doctor whoaverages $14,000.
Everyone wants to get over tothe left-hand side Lower cost is
better.
The unfortunate reality, though,of how we've done data and
analytics in identifying doctorswith this episode grouper
approach is it's highlyinaccurate.
(06:32):
So you could have one or twopatients who maybe the doctor
did all the clinicallyappropriate things.
They prescribed them in alow-cost generic medication, but
all of a sudden that patienthas an allergic reaction to that
generic drug and needs to go onto a higher cost brand name
medication.
When you do things like episodegroupers, it doesn't really
understand that and equate that.
(06:53):
And now you've got a doctorwho's done all the right things
maybe the doctor on the righthere and they're getting dinged
or a higher cost score for thatpatient, and it's kind of
skewing them.
And then, if you imagine, maybethey only have 20, 30 patients
worth of patients in that data,you could see how this could get
skewed year over year.
And so what we found is it'shighly inaccurate and you're
going to give recognition to onedoctor, but maybe that's not
(07:14):
the best approach, and so whatwe've done here is kind of again
, use data, use technology.
We use a lot of technology toactually grab the latest
peer-reviewed medical literatureout there to help inform our
team on how to accuratelyidentify top performing doctors,
and so what you can do isactually start to go in a much
more granular fashion tounderstand doctor performance.
(07:37):
How likely are you just to jumpright to surgery before trying
physical therapy?
Again, about a third of allhealthcare out there, those
surgeries are unnecessary, andso if you can get more people to
try physical therapy first,alleviate pain, alleviate
pressure and maybe they're backon their feet, that's obviously
a great outcome, not only from acost perspective.
(07:59):
But I don't think anyone inthis room wants to get surgery
when you could go for six, eightPT sessions and avoid that
procedure, and so you can see astark difference here between
the doctors on the left and theright.
We're then going to look atwhat types of techniques do they
actually use when they'reperforming their surgeries?
What are the complication ratesafter you have that particular
(08:19):
procedure?
Revision rates, what percent ofthe surgeries are going to be
done in an inpatient setting,really high cost, versus maybe
an outpatient ambulatory setting.
What are the costs associatedwith those negotiated rates
between the providers, thefacilities that they utilize and
the payers?
And what we've done is kind ofcome up with this new approach
(08:40):
to really understand whichdoctor you actually want to see.
And you could see here a starkdifference in a rating when you
go kind of a bottoms upmethodology versus a traditional
episode grouper approach.
And so obviously it's prettyclear, I think here, looking at
the data, that you want to go tothe right instead of the left,
whereas as a nation we've kindof always operated if you could
(09:01):
even get access to informationwith the model up on the right.
And so what we've done isactually started to show the
results for employers that we'veworked with.
Again, we are fortunate enoughto actually get the data and be
able to mine that with ourtechnology, and so before kind
of utilizing that approach thatI showed before, you can kind of
see the results.
(09:21):
So they had five people go tothe doctor on the left because
again it was deemed high quality.
After realizing the supplyingthe information, we were able to
actually change behavior toprovider on the right, and so
this is kind of what happened.
So you can see here they hadfive members see that doctor on
(09:42):
the left.
You can see jumping to surgery,revision rates, all of the
things that I just mentioned,and you could actually see the
total paid claims that thatemployer had attributed to just
one doctor, and you can imaginemany people seeing many doctors
like this on the left, and sowhat they also had was huge
member cost share.
So not only are employers beingsqueezed again as we approach
that 9% revenue threshold, wealso have an affordability
(10:04):
problem in this country wheremost folks can't actually cover
the deductible because theaverage cash on hand is actually
lower than their deductible.
So, even though you've gothealth insurance, you're
virtually uninsured.
And so what we did is again, usethe data, use the technology.
Let's get people over to thedoctor on the right-hand side,
and here's what actually canhappen.
(10:25):
So if you can leverage data,you can leverage technology.
You can give people, at time ofneed, useful information on
where they should be going fortheir care.
You can make a major impact,saving over $100,000 just for
this one particular employer,and you can see all of the great
clinical outcomes.
But the best part about it is,if you can share that
(10:46):
information and have people makeoptimal decisions, you can
eliminate the affordabilityproblem as well.
So any employers in theaudience that are covering or
sponsoring health benefits, youcould see easy trade.
I'll pick up the $18,000 foryou and we'll save $100,000 on
the top line, and that's how wecan really start to use data
technology to leverage win-winsituations for employers.
(11:09):
So the other thing that I wantedto talk about is how we should
all think about this stuffdifferently.
So what's interesting is I livein New Jersey but right outside
of New York City a lot of folksin here if you do any traveling
for work, your company kind ofgives you a per diem for your
food when you're traveling, andso what I thought I would do is
kind of highlight here if we'relooking at the price of a
(11:31):
restaurant in New York City,there's obviously huge
variations in the cost.
You can, you know, go toChipotle, or you can go to a you
know five star restaurant, butthe company say, hey, basically
we're gonna give you a flat rate.
You can choose to go to the$1,000 dinner if that's what you
want to do, or you can chooseto stay below budget.
That's really up to you, butwe're going to cap our exposure
(11:52):
for your meals and that's goingto kind of be how you have to go
purchase that.
Well, what's interesting ispeople think, well, I've given
health insurance benefits, so aslong as you go in the network,
we'll pay whatever it is.
But what's very clear is thatthere's huge costs, as I just
mentioned, associated with whichdoctors you see, and so we have
to start to think about thisstuff a little bit differently.
(12:13):
Why is it okay to limit, kindof the cost of a meal and make
sure people want to go and havea thousand dollar dinner at the
five-star restaurant?
Great.
But we as the employers are notgoing to sponsor that.
Whereas with health insuranceit's kind of well, go on the
network, we don't care what itactually costs, and you can see
that how that's starting tocreate a big problem there.
So the other thing is a lot offolks think, well, the $1,000
(12:36):
dinner, it's a great experience,the food is really good, the
service is great, so you getwhat you pay for.
The unfortunate reality is, inhealthcare that's actually not
the same.
So sometimes when you're endingup at those higher cost doctors
that I showed on the right handside, you actually are paying
more and getting less, meaningthere's doctors unfortunately
(12:56):
out there who are just you know,they own the imaging center
across the street, so anyonethat walks in, you're going
across the street and you'regetting that image, whether
right or wrong, and so there'shuge impacts to cost and quality
, again by the doctor and it'snot really correlated.
So, just highlighting herecomplication rates this is in
New York City and you can seethat you can get really low
(13:16):
complication rates for a faircost, where there's also doctors
that are really high cost withhigh complication rates and
there's no rhyme or reason towhy members are choosing one or
the other.
The other thing is that there'sa lot of emerging technology.
Obviously we're learning aboutAI, but also in healthcare.
So over the last couple ofyears, glp-1s have been a huge
(13:38):
hot topic of debate everyonetalking about that and you can
see there's been explosivegrowth on GLP-1s, but
particularly for low value care.
And what we found is the sameidea applies to prescriptions.
Right, there are certaindoctors that are going to just
write patients a script becausethey walk into the office and I
want my Wegovi or whatever it isthat they want, and then
(14:00):
there's going to be doctors whoare really going to follow
clinically best practices andonly prescribe medication that's
truly going to help and iswarranted for that specific
patient.
And so you can see here Ishowed it earlier on
complication rates, revisionrates, but this is something
that we're constantly doing andreviewing the data on is really
GLP-1s, and you can see bydoctor.
There's basically a 6x rate interms of how much wasteful care,
(14:23):
meaning they're up-codingpatients that aren't clinically
necessarily appropriate for thatprescription.
They're obviously not makingsure that the patients are
adhering to it and that meetingwith them and checking in.
So huge again cost qualityimplications.
And the reality is, if youdon't have the data and you're
not looking at it, there's notreally a way to make an impact.
So how can we actually usetechnology and data to help with
(14:47):
this problem?
What's interesting is there's alot of new technologies coming
forward for healthcare,particularly alternative-driven
health plans.
So these are new approaches tohealthcare.
What they are?
Narrow networks or a selectnetwork of doctors who want to
drive as many people there.
That'll hopefully help the costproblem.
The interesting thing, though,is how many people have tried to
(15:09):
go out to their carrierdirectory and find a doctor and
had success in making sure thatdoctor's phone number's right,
the address is right, they'reactually in the network, they're
accepting new patients, there'snot an eight-month wait time to
actually go and see thatprovider.
It's pretty problematic and I'mgoing to show you some stats.
But reality is most directoriesare not very accurate and
(15:30):
what's happening is, as thesenew technologies and data
sharing is occurring out in theworld, you're starting to see
more and more people rely ondirectories, particularly with
their carriers or their healthplan, to find top doctors that,
again, are going to drive highquality outcomes.
And so what we're seeing is theprevious state only about 11%
of people went to a carrierwebsite to find doctors.
(15:52):
What happened now is, withthese value driven health plans,
is you got about 64% of folkslooking at those directories to
try and find providers?
The reality is it's only 27%accurate.
So you got a ton of demand nowgoing towards these directories
to try and find high qualitycare, but the reality is again
(16:13):
are they in the network?
Are they actually accepting newpatients?
Do they have reasonableappointment availability?
All of those things kind offactor into the experience of
that member, and the reality isit's not really gonna help, and
so, again, odds of being able tosuccessfully call one of those
first five phone numbers youhave about a 28% likelihood of
that actually happening.
(16:33):
And so what happens is a lot ofmembers out there try and
engage, try and go to do theright thing.
They check with one of thesetools and unfortunately you
can't actually go and see thoseproviders, whereas if you can
use technology and artificialintelligence to clean up
provider directories, you reallycan make a meaningful impact
and actually have only about a2% issue.
And so what I want to talk abouthere and this will kind of play
(16:56):
off to the side here isactually leveraging AI and
machine learning to helpeliminate some of that.
So what we've actually done isbuilt proprietary tools
internally where we could say,hey, go out to this specific
hospital system, this specificdoctor, I'm interested in
understanding when the nextappointment availability will be
, I'm looking for an in-personvisit, I'm a new patient, etc.
(17:19):
And what you could see here isthat the technology actually is
smart enough to go out, findthis specific doctor.
They have online appointmentbooking technology.
You can go in, pick thelocation, and, again, this is
not being done by a human, thisis all computer.
And what you could see is itcan actually help and listen to
the prompt that you put intothere and then it's going to
(17:41):
populate.
Hey, here's actually the nextappointment for this person, and
you can imagine usingartificial intelligence to
actually help enhance healthcareexperiences with something
simple like that.
Now you know that this doctorif we're going to recommend Dr
Lin here has appointmentavailability coming up.
This was a couple weeks ago,but it was February 25th at 11
(18:01):
am, and you can share that realtime with members and that
creates a really awesomeexperience when you're trying to
figure out what doctor to go to.
And so what we found is, byleveraging artificial
intelligence and basicallyutilizing tools and technology
to help clean up something sosimple that's been around and
plagued forever provideraccuracy you can actually use
(18:23):
that stuff to actually get abouta 90% accuracy rating that the
doctors are in your network,they have appointment
availability, they're acceptingnew patients, so on and so forth
.
And so, again, reallyleveraging this technology to
help do it.
And so, in closing, I just wantto talk about kind of how we're
leveraging all of this stuff toreally help make an impact for
those of you interested incurbing healthcare costs.
(18:44):
And so, again, what we found isthere's a lot of things out
there, but they're not reallymoving the needle as it relates
to cost and quality.
It's really hard to get peopleto engage with information again
, particularly if it's going tobe inaccurate and not provide a
great experience.
And the other thing ishealthcare is personal and it
can't be really disruptive, andso a lot of things that have
(19:06):
come out.
They're good in theory, butfolks don't really want to
engage with it, whereas what wefound is you can use the data,
you can use technology, you canuse artificial intelligence.
You can really help supportmembers by adding to your
benefit plan something that'sgoing to accurately identify
rankings, and we brought back DrGraf here.
We actually have found that youcan utilize incentives.
(19:28):
Again, I showed you the abilityto drastically lower costs and
offset that by helping peoplepay their out-of-pocket expenses
while also keeping yourexisting network, and so what
that does is it generatessignificant savings across all
of the information we haveaccess to.
The doctor on the left versusthe doctor on the right that I
showed you can save about 27%every time you move people over
(19:52):
to the right.
And again, as we think aboutclimbing towards that 9% revenue
number, the more we can getthat 27% savings, the better you
can get in lowering healthcarecosts.
Utilizing incentives toactually get people to engage
with the data and find higherquality care is super important
around generating engagement,and then what that will actually
(20:12):
lead to is overall reduction inplan costs.
So, again, as we creep towards9%, imagine immediately being
able to impact your personalspend by 12%, and so what that
really does is creates a uniqueexperience where you can
leverage data, you can leveragetechnology, a great user
experience to really providequality outcomes to hopefully
(20:33):
help this entire room kind ofavoid the march to the 9%
revenue number that I talkedabout earlier.
Thanks for tuning in to Riskand Resolve.
See you next time.
You're listening to Risk and Resolve.
And now for your hosts, benConner and Todd Hufford.
I'm Steve Santangelo.
As mentioned, I got in latelast night from New Jersey where
I live.
I do have a toddler and I'vedone the cookie battle many a
time, so I can resonate withthat.
But basically last night got tomy hotel, was thinking about
healthcare, which is reallywhere I'm focused.
(00:22):
I know Ben showed that awesomevideo highlighting kind of some
of the struggles that we'reseeing in healthcare today and
then thinking about AItechnology in the future and
kind of how to bring that alltogether in my presentation here
today.
And so what did I do in my hotelroom late last night?
And I went to chat GPT and Isaid how do you give a good
presentation?
And so there's basically twothings you got to do.
The first is introduce yourselfand you made me sound way more
(00:45):
important than I am.
And then two, you start withsomething thought provoking,
maybe a question or somethinglike that.
So for the audience, going backto 2008, huge financial crisis,
gm at the forefront of that.
Does anyone know what reallycaused that for GM?
What was the real impact totheir business that caused that
massive financial pressure?
The real impact to theirbusiness that caused that
(01:06):
massive financial pressure,didn't say if no one answers
what to do, so I'm going to goforward.
Anyway, basically this isreally the problem.
So GM got to a point wheretheir revenue percent of their
revenue 9% was attributed totheir pensions and benefit
offerings.
So think about that 9% goingjust towards that.
So that basically led WarrenBuffett to come out and
(01:27):
basically say GM's really not abusiness.
What they are is a huge annuity, an insurance company with
basically a major auto companyattached, and so that is pretty
powerful to see there.
Warren Buffett has also beenquoted as saying basically the
American healthcare system iskind of a tapeworm on the
American economy.
It gets more expensive everyyear.
You kind of saw that in Ben'svideo earlier.
(01:48):
It's just costs are continuingto go up and unfortunately
incentives are very misalignedacross the entire industry to
really impact that.
And so what's going on in theworld and again, I've been in
healthcare now over 15 yearswe're kind of all headed in that
direction.
That GM was, and so latestestimates basically say that by
2031, all employers in theentire United States are
(02:12):
basically going to be at orexceed that 9% of revenue going
towards benefit packages.
So we're kind of headed on acrash course.
We've been on that journey forquite some time.
Ben can speak veryintelligently to that, but when
I started at UnitedHealthcareover 15 years ago, we always
said if a premium got to $500,the whole system's going to shut
(02:34):
down.
Many of you in here areprobably paying double, if not
triple, that, depending upon ifyou're enrolled with families
and things of that nature.
So it's really an unsustainablecourse and unfortunately we got
to start to think differentlyif we're going to make an impact
on that.
And that's where I think all ofthese great things data
technology, ai can really helpus kind of tackle that problem.
(02:55):
So the thing that we've donehere at Garner is basically
looked at over 75% of all of themedical claims in the entire
country and that's a big dataset.
That requires a lot oftechnology machine readable
files, ai, things of that natureand what we said is well, what
is it that really impacts, costthe most?
(03:16):
And so what we found by usingthe data is really there's one
thing and it's really whichindividual doctor a patient sees
has the greatest impact on thecost of care, and if we can get
a lot of people to start seeingreally high quality doctors, we
can actually reverse that courseand start to bring down the
expected trend of healthcare.
(03:37):
And so what we've done iswritten over basically 500
different metrics, both qualityand cost focused.
But just wanted to highlightsome of the things that we see
in the data around.
First, complication rates frommajor surgery.
If you look at the top 25% ofdoctors, they average about a 5%
or better complication rate.
(03:58):
If you look at the bottom 25%of providers, you have almost a
20% complication rate, and again, that's just after a major
surgery.
But that applies basically toany healthcare needs you could
ever have.
And then the other thing thatwe're hearing out there.
A lot of people have talkedabout fraud, waste, abuse in the
healthcare system.
The reality is is that about athird of all healthcare out
(04:19):
there is deemed unnecessary,meaning it's a procedure, it's a
test, it's a drug, it'swhatever.
That should have never actuallyhappened.
And imagine that if we couldeliminate 30% of all healthcare
spend in the country againreally starting to bring down
that cost of care and sohighlighting kind of
inappropriate replacements ofjoints again unnecessary care
you get people to the top 25% ofdoctors.
(04:42):
That's sub 10%, unfortunately,if you end up seeing a bottom
25% provider, that's near 40%.
And so that's it from kind of aquality perspective.
There's also huge costimplications.
Obviously, a surgery that'sunnecessary is a huge cost
burden.
But you can see here, you know,just the basic cost of an
(05:02):
office visit.
So you get people to the top25% of doctors somewhere around
100 bucks.
Bottom 25, you're approachingto $250.
And then if you think aboutcosts of knee replacements,
there's, you know, huge costvariations just on the
negotiated rate between thepayers and the providers and you
see a staggering differencethere.
Top 25% of doctors you know inthe ballpark of $13,000, almost
(05:27):
$25,000 if you go to the bottomperforming doctors.
So what I wanted to do andhopefully these are no one's
relatives here, but we're gonnastart naming names and talking
about kind of what's been donein the world as it relates to
identifying high qualityperformers and kind of how
Garner's taking a differentapproach using the technology
(05:48):
and some of that machinelearning, and so historically
all doctors have been scored onthis thing called episode
groupers.
You basically take a wholebunch of patients attributed to
that doctor, you divide up theaverage cost per patient for a
specific set of codes, whateverthe condition might be, and you
basically get a total cost ofcare.
Again, I think we're allaligned here that having lower
(06:10):
cost is a good outcome, and soin that traditional approach,
you get one doctor who averages$13,000.
You have another doctor whoaverages $14,000.
Everyone wants to get over tothe left-hand side Lower cost is
better.
The unfortunate reality, though,of how we've done data and
analytics in identifying doctorswith this episode grouper
approach is it's highlyinaccurate.
(06:32):
So you could have one or twopatients who maybe the doctor
did all the clinicallyappropriate things.
They prescribed them in alow-cost generic medication, but
all of a sudden that patienthas an allergic reaction to that
generic drug and needs to go onto a higher cost brand name
medication.
When you do things like episodegroupers, it doesn't really
understand that and equate that.
(06:53):
And now you've got a doctorwho's done all the right things
maybe the doctor on the righthere and they're getting dinged
or a higher cost score for thatpatient, and it's kind of
skewing them.
And then, if you imagine, maybethey only have 20, 30 patients
worth of patients in that data,you could see how this could get
skewed year over year.
And so what we found is it'shighly inaccurate and you're
going to give recognition to onedoctor, but maybe that's not
(07:14):
the best approach, and so whatwe've done here is kind of again
, use data, use technology.
We use a lot of technology toactually grab the latest
peer-reviewed medical literatureout there to help inform our
team on how to accuratelyidentify top performing doctors,
and so what you can do isactually start to go in a much
more granular fashion tounderstand doctor performance.
(07:37):
How likely are you just to jumpright to surgery before trying
physical therapy?
Again, about a third of allhealthcare out there, those
surgeries are unnecessary, andso if you can get more people to
try physical therapy first,alleviate pain, alleviate
pressure and maybe they're backon their feet, that's obviously
a great outcome, not only from acost perspective.
(07:59):
But I don't think anyone inthis room wants to get surgery
when you could go for six, eightPT sessions and avoid that
procedure, and so you can see astark difference here between
the doctors on the left and theright.
We're then going to look atwhat types of techniques do they
actually use when they'reperforming their surgeries?
What are the complication ratesafter you have that particular
(08:19):
procedure?
Revision rates, what percent ofthe surgeries are going to be
done in an inpatient setting,really high cost, versus maybe
an outpatient ambulatory setting.
What are the costs associatedwith those negotiated rates
between the providers, thefacilities that they utilize and
the payers?
And what we've done is kind ofcome up with this new approach
(08:40):
to really understand whichdoctor you actually want to see.
And you could see here a starkdifference in a rating when you
go kind of a bottoms upmethodology versus a traditional
episode grouper approach.
And so obviously it's prettyclear, I think here, looking at
the data, that you want to go tothe right instead of the left,
whereas as a nation we've kindof always operated if you could
(09:01):
even get access to informationwith the model up on the right.
And so what we've done isactually started to show the
results for employers that we'veworked with.
Again, we are fortunate enoughto actually get the data and be
able to mine that with ourtechnology, and so before kind
of utilizing that approach thatI showed before, you can kind of
see the results.
(09:21):
So they had five people go tothe doctor on the left because
again it was deemed high quality.
After realizing the supplyingthe information, we were able to
actually change behavior toprovider on the right, and so
this is kind of what happened.
So you can see here they hadfive members see that doctor on
(09:42):
the left.
You can see jumping to surgery,revision rates, all of the
things that I just mentioned,and you could actually see the
total paid claims that thatemployer had attributed to just
one doctor, and you can imaginemany people seeing many doctors
like this on the left, and sowhat they also had was huge
member cost share.
So not only are employers beingsqueezed again as we approach
that 9% revenue threshold, wealso have an affordability
(10:04):
problem in this country wheremost folks can't actually cover
the deductible because theaverage cash on hand is actually
lower than their deductible.
So, even though you've gothealth insurance, you're
virtually uninsured.
And so what we did is again, usethe data, use the technology.
Let's get people over to thedoctor on the right-hand side,
and here's what actually canhappen.
(10:25):
So if you can leverage data,you can leverage technology.
You can give people, at time ofneed, useful information on
where they should be going fortheir care.
You can make a major impact,saving over $100,000 just for
this one particular employer,and you can see all of the great
clinical outcomes.
But the best part about it is,if you can share that
(10:46):
information and have people makeoptimal decisions, you can
eliminate the affordabilityproblem as well.
So any employers in theaudience that are covering or
sponsoring health benefits, youcould see easy trade.
I'll pick up the $18,000 foryou and we'll save $100,000 on
the top line, and that's how wecan really start to use data
technology to leverage win-winsituations for employers.
(11:09):
So the other thing that I wantedto talk about is how we should
all think about this stuffdifferently.
So what's interesting is I livein New Jersey but right outside
of New York City a lot of folksin here if you do any traveling
for work, your company kind ofgives you a per diem for your
food when you're traveling, andso what I thought I would do is
kind of highlight here if we'relooking at the price of a
(11:31):
restaurant in New York City,there's obviously huge
variations in the cost.
You can, you know, go toChipotle, or you can go to a you
know five star restaurant, butthe company say, hey, basically
we're gonna give you a flat rate.
You can choose to go to the$1,000 dinner if that's what you
want to do, or you can chooseto stay below budget.
That's really up to you, butwe're going to cap our exposure
(11:52):
for your meals and that's goingto kind of be how you have to go
purchase that.
Well, what's interesting ispeople think, well, I've given
health insurance benefits, so aslong as you go in the network,
we'll pay whatever it is.
But what's very clear is thatthere's huge costs, as I just
mentioned, associated with whichdoctors you see, and so we have
to start to think about thisstuff a little bit differently.
(12:13):
Why is it okay to limit, kindof the cost of a meal and make
sure people want to go and havea thousand dollar dinner at the
five-star restaurant?
Great.
But we as the employers are notgoing to sponsor that.
Whereas with health insuranceit's kind of well, go on the
network, we don't care what itactually costs, and you can see
that how that's starting tocreate a big problem there.
So the other thing is a lot offolks think, well, the $1,000
(12:36):
dinner, it's a great experience,the food is really good, the
service is great, so you getwhat you pay for.
The unfortunate reality is, inhealthcare that's actually not
the same.
So sometimes when you're endingup at those higher cost doctors
that I showed on the right handside, you actually are paying
more and getting less, meaningthere's doctors unfortunately
(12:56):
out there who are just you know,they own the imaging center
across the street, so anyonethat walks in, you're going
across the street and you'regetting that image, whether
right or wrong, and so there'shuge impacts to cost and quality
, again by the doctor and it'snot really correlated.
So, just highlighting herecomplication rates this is in
New York City and you can seethat you can get really low
(13:16):
complication rates for a faircost, where there's also doctors
that are really high cost withhigh complication rates and
there's no rhyme or reason towhy members are choosing one or
the other.
The other thing is that there'sa lot of emerging technology.
Obviously we're learning aboutAI, but also in healthcare.
So over the last couple ofyears, glp-1s have been a huge
(13:38):
hot topic of debate everyonetalking about that and you can
see there's been explosivegrowth on GLP-1s, but
particularly for low value care.
And what we found is the sameidea applies to prescriptions.
Right, there are certaindoctors that are going to just
write patients a script becausethey walk into the office and I
want my Wegovi or whatever it isthat they want, and then
(14:00):
there's going to be doctors whoare really going to follow
clinically best practices andonly prescribe medication that's
truly going to help and iswarranted for that specific
patient.
And so you can see here Ishowed it earlier on
complication rates, revisionrates, but this is something
that we're constantly doing andreviewing the data on is really
GLP-1s, and you can see bydoctor.
There's basically a 6x rate interms of how much wasteful care,
(14:23):
meaning they're up-codingpatients that aren't clinically
necessarily appropriate for thatprescription.
They're obviously not makingsure that the patients are
adhering to it and that meetingwith them and checking in.
So huge again cost qualityimplications.
And the reality is, if youdon't have the data and you're
not looking at it, there's notreally a way to make an impact.
So how can we actually usetechnology and data to help with
(14:47):
this problem?
What's interesting is there's alot of new technologies coming
forward for healthcare,particularly alternative-driven
health plans.
So these are new approaches tohealthcare.
What they are?
Narrow networks or a selectnetwork of doctors who want to
drive as many people there.
That'll hopefully help the costproblem.
The interesting thing, though,is how many people have tried to
(15:09):
go out to their carrierdirectory and find a doctor and
had success in making sure thatdoctor's phone number's right,
the address is right, they'reactually in the network, they're
accepting new patients, there'snot an eight-month wait time to
actually go and see thatprovider.
It's pretty problematic and I'mgoing to show you some stats.
But reality is most directoriesare not very accurate and
(15:30):
what's happening is, as thesenew technologies and data
sharing is occurring out in theworld, you're starting to see
more and more people rely ondirectories, particularly with
their carriers or their healthplan, to find top doctors that,
again, are going to drive highquality outcomes.
And so what we're seeing is theprevious state only about 11%
of people went to a carrierwebsite to find doctors.
(15:52):
What happened now is, withthese value driven health plans,
is you got about 64% of folkslooking at those directories to
try and find providers?
The reality is it's only 27%accurate.
So you got a ton of demand nowgoing towards these directories
to try and find high qualitycare, but the reality is again
(16:13):
are they in the network?
Are they actually accepting newpatients?
Do they have reasonableappointment availability?
All of those things kind offactor into the experience of
that member, and the reality isit's not really gonna help, and
so, again, odds of being able tosuccessfully call one of those
first five phone numbers youhave about a 28% likelihood of
that actually happening.
(16:33):
And so what happens is a lot ofmembers out there try and
engage, try and go to do theright thing.
They check with one of thesetools and unfortunately you
can't actually go and see thoseproviders, whereas if you can
use technology and artificialintelligence to clean up
provider directories, you reallycan make a meaningful impact
and actually have only about a2% issue.
And so what I want to talk abouthere and this will kind of play
(16:56):
off to the side here isactually leveraging AI and
machine learning to helpeliminate some of that.
So what we've actually done isbuilt proprietary tools
internally where we could say,hey, go out to this specific
hospital system, this specificdoctor, I'm interested in
understanding when the nextappointment availability will be
, I'm looking for an in-personvisit, I'm a new patient, etc.
(17:19):
And what you could see here isthat the technology actually is
smart enough to go out, findthis specific doctor.
They have online appointmentbooking technology.
You can go in, pick thelocation, and, again, this is
not being done by a human, thisis all computer.
And what you could see is itcan actually help and listen to
the prompt that you put intothere and then it's going to
(17:41):
populate.
Hey, here's actually the nextappointment for this person, and
you can imagine usingartificial intelligence to
actually help enhance healthcareexperiences with something
simple like that.
Now you know that this doctorif we're going to recommend Dr
Lin here has appointmentavailability coming up.
This was a couple weeks ago,but it was February 25th at 11
(18:01):
am, and you can share that realtime with members and that
creates a really awesomeexperience when you're trying to
figure out what doctor to go to.
And so what we found is, byleveraging artificial
intelligence and basicallyutilizing tools and technology
to help clean up something sosimple that's been around and
plagued forever provideraccuracy you can actually use
(18:23):
that stuff to actually get abouta 90% accuracy rating that the
doctors are in your network,they have appointment
availability, they're acceptingnew patients, so on and so forth
.
And so, again, reallyleveraging this technology to
help do it.
And so, in closing, I just wantto talk about kind of how we're
leveraging all of this stuff toreally help make an impact for
those of you interested incurbing healthcare costs.
(18:44):
And so, again, what we found isthere's a lot of things out
there, but they're not reallymoving the needle as it relates
to cost and quality.
It's really hard to get peopleto engage with information again
, particularly if it's going tobe inaccurate and not provide a
great experience.
And the other thing ishealthcare is personal and it
can't be really disruptive, andso a lot of things that have
(19:06):
come out.
They're good in theory, butfolks don't really want to
engage with it, whereas what wefound is you can use the data,
you can use technology, you canuse artificial intelligence.
You can really help supportmembers by adding to your
benefit plan something that'sgoing to accurately identify
rankings, and we brought back DrGraf here.
We actually have found that youcan utilize incentives.
(19:28):
Again, I showed you the abilityto drastically lower costs and
offset that by helping peoplepay their out-of-pocket expenses
while also keeping yourexisting network, and so what
that does is it generatessignificant savings across all
of the information we haveaccess to.
The doctor on the left versusthe doctor on the right that I
showed you can save about 27%every time you move people over
(19:52):
to the right.
And again, as we think aboutclimbing towards that 9% revenue
number, the more we can getthat 27% savings, the better you
can get in lowering healthcarecosts.
Utilizing incentives toactually get people to engage
with the data and find higherquality care is super important
around generating engagement,and then what that will actually
(20:12):
lead to is overall reduction inplan costs.
So, again, as we creep towards9%, imagine immediately being
able to impact your personalspend by 12%, and so what that
really does is creates a uniqueexperience where you can
leverage data, you can leveragetechnology, a great user
experience to really providequality outcomes to hopefully
(20:33):
help this entire room kind ofavoid the march to the 9%
revenue number that I talkedabout earlier.
Thanks for tuning in to Riskand Resolve.
See you next time.
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