Inside CMS: How Medicare Innovation Models Are Working to Lower Costs & Scale Digital Health
Health Affairs Publishing’s Rob Lott speaks to Abe Sutton, Director of the Center for Medicare and Medicaid Innovation, about the ACCESS model and broader efforts to test payment and delivery reforms aimed at improving affordability, expanding digital health, and generating real-world evidence in Medicare.
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Rob Lott: Friends, it's another very special episode of A Health
Podyssey. And today, it's a real privilege to welcome Abe Sutton,
Deputy Administrator for the Centers for Medicare and
Medicaid Services and Director of the Center for Medicare and
Medicaid Innovation, the Innovation Center. And as
regular listeners know, the Innovation Center was created
under the Affordable Care Act to test novel healthcare payment
and delivery models. Now fifteen years later, it is the beating
heart of the federal government's efforts to spark
transformation in healthcare. Abe Sutton, welcome to our
Humble podcast.
Abe Sutton: Thank you for having me on. It's a privilege to be
here.
Rob Lott: And, we asked Abe to come and talk about one model in
particular, the access model, and we'll dig into that in just
a moment. But before we do, I thought maybe we could take a
step back and just talk briefly about the Innovation Center's
work writ large. So if you're ready, we can dive right in.
Abe Sutton: Let's go for it. Maybe just start. Our focus is
really on increasing the affordability of healthcare in
this country. If I think about the statutory mandate, it's
about improving quality and lowering costs. We have a world
class healthcare system.
We have new innovations that are available. We have advancements
and different strategies for giving care, but too many
people, cannot engage with that system. Seek care is really
inaccessible and unaffordable, in America today. I'm not only
talking about the Medicare, Medicaid programs who refocus,
but in the employer market as well. I think of our purpose in
the innovation center as changing the incentive
structures, payment and delivery reform so that care can be
delivered more affordably in America.
So we change the way that people think and approach the market
and care really becomes more accessible. To break that down
into a few buckets, if I may, we have 10 new models that we've
announced so far in this term. Four focus on the affordability
of drugs in this country and continuing to incentive as
advancement on pharmaceuticals. Three are about outcome aligned
payments. ACO models, the access model that I know we'll touch
on, and really about alignment of incentives so that you're
focused on delivering the right outcome.
And then the third bucket is really around prevention. It's
around getting upstream. It's around, the basic research on
what different interventions and wellness interventions can make
a difference upstream. And so I think that through line across
all of them is about making care more affordable and more
accessible.
Rob Lott: So if we look back over the past fifteen years of
the center, it's obviously tested dozens and dozens of
different models. You just said, you know, there are 10 more in
this past year. But so far, most of the models that have sort of
worked their way through this system have not met the criteria
for nationwide expansion after evaluation. Why is that so hard?
Think of
Abe Sutton: our job as akin to a venture capitalist. It's not
about how many of your models work versus how many do not.
It's about what is the cumulative impact of the models
that work and do they justify the investment that has been
made? And so a classic VC model, maybe you do 20 investments and
one of them works and returns all the costs of what you put
into the 20 investments and then some. And that's the classic
model there.
It encourages bold bets. It's not about how many work, it's
about the cumulative impact on the healthcare system. And so if
I think about a model that really means that people are
getting better care, that really means we're going upstream and
prevent it and works, it could pay for running 10 models that
it turns out were miscalibrated in some way or didn't work
because we were taking bold bets. I'm okay with that. And I
think that was the intent of having an innovation center.
We have learned from practically every model that has happened in
the center's history. Some of them we've learned, okay, if you
change the incentive in this way, it won't move the needle
enough because you won't have a large enough mindshare. And
others we've learned, oh, you just need to recalibrate it this
way to account for that externality and have a quality
measure on that. And then you could retest it in a different
way and we could iterate. We've also seen things where maybe we
didn't certify the model, but we were able to draw out an
approach and actually embed it within the existing statutory
authority of how Medicare works.
And so there are many different ways to approach payment in
healthcare, and we've pushed forward learnings through these
different iterative approaches. I'll also call out like a more
focused portfolio as a result of this iteration. So we did a ton
of episodes. Bundles and episodes, I think, were all the
rage at the start of the Innovation Center's history, as
your listeners will know well, just given this audience. And
episodes theoretically make a ton of sense.
So we have episodes in our portfolio in what we do, but we
can't get episodes right always. It's hard to calibrate the exact
time length. Should this be thirty days, forty five, sixty,
ninety? What code should be in the episode versus what code
should be outside of the episode? Oh, did we miss one
where then there's a surge of utilization on that code apart
from this?
Is this the right setting? Should we include the post acute
setting in how we're thinking about this? And I say all this
to demonstrate is that it's really hard to calibrate and do
an episode well. We're trying to shift on more on ACOs and more
on some of the other areas where we could push forward innovation
and open source the design of episodes going forward, which is
what our CMS administered risk arrangements are all about. It's
about letting us collect data as ACOs and specialists enter their
own arrangements on that.
I look at the history of the center and I actually think it's
pretty reasonable for what we set out to do. And I also think
that the return looks better in the future, both on quality and
on costs due to the way that models have now been calibrated
and the learnings we've had about how to design them.
Rob Lott: Fair enough. Well, perhaps we can look at the Model
as sort of a case study for some of those challenges that you're
navigating. As you know, obviously, Access Model aims to
increase access to technology supported care services for
people with traditional Medicare. And in the past,
Medicare hasn't had a straightforward mechanism to pay
for this kind of care. We're talking about apps and online
patient portals and various technology supported services.
And so under this model, these companies will now begin to be
reimbursed under Medicare Part B. There's an outcomes aligned
payment approach to it where organizations receive recurring
payments for managing qualifying conditions with full payment
tied to measurable health outcomes. So in that context,
some have raised concerns, including author Maurice Shah
and colleagues in recent pages of Health Affairs Forefront,
that because digital health companies have usually a pretty
sophisticated patient acquisition capabilities, this
model might be particularly prone to cherry picking, to sort
of very targeted patient identification, which could then
sort of lead toward potential gaming around benchmarks. Is
there a risk of this happening, and do you share that concern?
Abe Sutton: I don't share that concern. And part of that
business was intensely debated and thought through in the
innovation center before the model went out. Part of that is
due to the design of the access model and the access models
quality measures. So let's talk about what it takes to drive
improvement, in this model. One, you need to have the patient
population engaged.
If the patient's engaged, you're getting the monthly payment, and
if not, you're not. Two, you need to show improvement and,
you need to show improvement on all of the quality measures
associated with the track for at least 50% of the population you
engage. So you don't need to be perfect, but you have to for 50%
drive improvement on all five metrics. And so just to bring
this to life a little for, the cardio, kidney, metabolic health
tracks, we're talking about things like A1C levels or BMI.
And what we do is we have you take a measure at the beginning
where we need actual data.
And then a year later, we're taking a measure of where they
are and you have to drive improvement, that surpasses the
targeted improvement amount for fifty percent of the patients
that you're engaging. What I like about this design is that
you're selecting people and you're actually improving how
they measure on these metrics. Even if you select people who
you think your strategy is likely to drive improvement,
okay, I don't consider that gaming. You're literally
improving people's health outcomes. That seems like a good
outcome.
And there might be other people who are better positioned to
drive improvement with a slightly different population.
So they'll have a different go to market and engage that other
population. The nice thing about our payment design coupled with
this quality measures and quality incentive structure is
that we're paying relatively little. So our calculation is
that if you could take a set of patients who are at one end of
the extreme of our measures here and just drive improvement in
the required amount for that patient population, it is worth
it for us to pay the amounts that we are paying here. The
other note I have is that the improvement is relative to where
the patient was.
So it's not like it's an arbitrary cutoff of you have a
BMI of thirty five and you have to get to thirty to show
improvement, but somebody who is thirty six also has to get to
thirty such that it's worth getting. It's about the
improvement relative to where the patient was. And so I think
there will be different strategies and different
interventions that are more effective at people with higher
BMIs or people with lower BMIs who are still too high for where
we want to be, and different companies will adopt different
strategies.
Rob Lott: Great. So another recent Health Affairs article,
this one by Andrew Rundle and colleagues, has pointed to the
fact that the Medicare population is quite different
from the population that up until now has typically used
these platforms. There are higher rates of limited English
proficiency, lower rates of health tech literacy, and that
that might make it harder for providers and these services to
achieve key outcomes, at least compared to how they've been
doing so far. What's your office thinking about this challenge
and how do you suggest they navigate it? I think
Abe Sutton: they're 100% right here. I think it is going to be
harder to be successful here. I think that is going to be
something that the participants in the model are going to need
to grapple with and in signing up for this, are taking on the
risk of, they do that? And I think that's what it should be.
This is a model and a test, and we want to see people come in
and innovate for different ways to do so, to the betterment of
the Medicare beneficiaries and for the benefit of the taxpayer
that funds this.
I think there's pretty good direct translation technology
out there nowadays that getting easily from one language to
another should be relatively straightforward, but the
likelihood of somebody to engage and trust the system and rely on
it easily is going to be different. I think engagement
broadly is going to be a challenge here that these
companies are going to need to take on and invest in if they're
going to be the solution that wins out and can scale and serve
millions of people in the original Medicare program. That
said, we have a pretty big incentive and reward for doing
so. The payments are low per patient, but if you scale to
original Medicare and there's no gatekeeper in the sense that a
Medicare Advantage or a commercial plan would have a
gatekeeper contracting that's in a select one of the successful
pilots to then offer nationwide to everyone, we're letting you
go direct to consumer. We're letting you go to physicians or
hospitals and say, this is right for your patients without a
gatekeeper review, as long as you meet our criteria.
So if you prove out that you are the best solution and we publish
those results on our website of here's how all the participants
are doing, that's a pretty big market opportunity for you. And
so there's a real incentive to invest in trying to improve and
reach the patient population that access serves. And I think
that is important because I don't want to live in a country
where the only people who could benefit from care accessible via
digital therapeutics between office visits or those who could
afford to pay out of pocket, or those who have a very, very good
commercial plan. I want people in Medicare, in the original
Medicare program, in Medicaid to have access to these supports as
well, is I think that's where we can potentially see the largest
benefit of some of these tracking, of some of these
prompts. And so I want that to be accessible to the Americans
who benefit and are on the programs that the Innovation
Center has accountability for.
Rob Lott: I want to ask a little bit about the role of primary
care physicians in this model. And my understanding is that in
addition to paying the health technology companies, there's
also a piece where physicians receive a small per member per
month payment for reviewing digital health plans and
coordinating care. Some have said that that payment is too
small, especially if these same providers are bearing potential
liability related to providing care to these patients through
these platforms. How are you expecting primary care
physicians to respond to these rates, and how are you thinking
about that in the context of the broader model?
Abe Sutton: It was really important to me when setting
this up to impose an obligation on participants to share
information back to the providers that people engage
with. And so the participants have an obligation, not just to
share something with health information, exchange, but to
actually take information and push it to, the designated
provider that a patient works with. That is important. It's
the first time that we've really imposed a push to a specific
provider as opposed to sharing in a network and then letting
the provider pull. And I thought that was important just for
connectivity and continuity of care.
So let me just say that. We also wanted to then give something to
make it economical to ingest that data and review it as
you're sitting with a patient and giving them guidance for
their care as a physician or as a clinician. And so that's where
the co management piece of this emerged. And it's really about
having your systems configured to ingest that and then
incorporating that in the care plan for the patient. So there's
that connectivity in place.
And so we wanted to reward and incentivize this. And what we're
going to do is closely monitor both how that plays out and
uptake broadly in the model, and then learn and iterate over
time. And so it's helpful to have feedback from folks. We
encourage folks to share their perspective with us. We'll also
look closely at the data on how this is playing out, what the
uptake is, and if this is proving effective and improving
the quality of health in this country and lowering the cost of
care in this country, because those are the ultimate metrics
that Congress has given us to focus on as a center.
Rob Lott: So is there a world where down the road you might
increase the physician payment rate?
Abe Sutton: Anything's possible, right? We'd have to see the
data. We'd have to look at what happens as the model plays out
and then react based off of that. I would just keep us
anchored on making sure that care is affordable for the
American people. I truly want to see us meet the moment of the
promising innovation of agentic solutions, augmented solutions
with a deflationary payment approach.
That means that the benefit of this flows out to our health
system broadly and the American consumer when they're engaging
with the healthcare system. Just thinking back to Meaningful Use,
we basically saw the digitization of health and
healthcare records, put in, in a way that didn't really seem to
change the cost trajectory, certainly not in a cost saving
direction. Let's think back to how we have bundles existing in
our healthcare system, but then as new technology advancements
came online, we had to have new technology add on payments
outside of the bundle because of the distorted incentive
structure that this created. I'd like to see access and I'd like
to see our broader governmental approach to paying for AI in
healthcare, not be something where we end up leading to
higher payments going out and more cost, because ultimately
that flows through to making care more unaffordable, not only
for Medicare and Medicaid beneficiaries and the programs
that pay for them, but in how it's referenced to the
commercial market as well. And so getting this right and
ensuring that our payments are appropriate and encourage
uptake, but do not inflate costs is important.
Rob Lott: One more question about the access model and we're
speaking here at Health Affairs where sort of the rigor of
evaluation and analysis is sort of at the heart of our mission.
And I know when we sort of think about how these models might be
sort of changing for a different population, that in the past,
these companies have sort of pitched their platforms to
corporate HR and benefits managers for whom the standards
of data and evidence might not always be sort of super rigorous
compared to, for example, physicians and physician
societies. And so I guess I'm thinking about sort of the new
potential customers that the model brings in, physicians who
might be looking for more peer reviewed studies as opposed to
PowerPoint slide decks? And big picture question here is about
sort of the maturation and the development of the evidence of
these technologies and the market. Do you expect there to
be a higher standard of evidence among physicians who are
expecting to refer patients to these programs?
And how does that potentially affect participation?
Abe Sutton: I think the first thing to start with here is our
evaluation strategy. And to inform that and draw real world
evidence and real world conclusions, we are using a
randomized approach where we're going to be taking one out of
every 10 beneficiaries, that sign up and we're going to put
them in a control group. And the reason we could do this is that
this is not an existing benefit. So this is an expansion of a
benefit to a new population. And so we're able to say, thank you
for showing you were qualified and expressing interest in
signing up with this participant.
You're in the control group. And, the benefit from that is
obviously we'll see how the people in the control group
compare to the people who are in the model itself. Now, if
participation is off the charts and we have millions of people
signing up, we obviously will need a lower number of people in
the control, lower percentage, that's just what is necessary to
draw a valid conclusion. If the participation is lower, we could
increase that. It's a model.
We have the ability to make changes and flex over time. But
I think we'll generate real world evidence on how the
Medicare population behaves, from the people who have opted
to sign up, who we put in to the model versus put into the
control. And we're going to put that for every participant out
on our website for everyone to access, and we'll show how
different participants compare with one another. And so this
will all be accessible to not only every doctor and every
hospital and every accountable care organizations, but frankly
to every beneficiary who wants to just go and look it up and
see. And I think that will impact uptake and direct it
towards those that are most effective at both engaging
people and improving their health outcomes.
And so will just say randomization, I think is an
incredibly powerful tool that we should consider more broadly in
our models. We can only do it when creating a new benefit
category because we can't restrict access for a
beneficiary to something already covered in Medicare, but I am
very confident and very excited in the design of how we're going
to evaluate this model. In fact, I might be more confident and
excited about the evaluation approach to the model than I am
about the model is transformative as I think
bringing digital therapeutics to the Medicare population and
original Medicare is going to be. The evaluation part is one
of the most exciting parts of this model.
Rob Lott: Cool. Well, you're speaking our language here at
Health Affairs and our audience as well, so I know I'm excited
to read that evaluation, and we'll look forward to seeing
what comes out of it. And I appreciate that you guys have
made that priority. Well, before we wrap up, thank you for really
digging into the weeds on the AXIS model. I really appreciate
it.
I did want to ask you about another set of innovation center
models, which is something you alluded to in your introduction,
which is those related to drug prices and various versions of
what what we're broadly calling most favored nation drug
pricing. You recently announced the postponement of the balanced
model, And then in addition to that, there's the generous
model, which is focused on states negotiating most favored
nation rates. There's the GLOBE model and the GUARD model.
Obviously, Medicaid has long had a low, you know, lowest price
program. And I guess I'm curious if you're worried that this sort
of wall of sound approach may be sending confusing signals or
multiple signals to the markets, and curious how, the Innovation
Center is coordinating all of these different programs where
there's a good amount of overlap and what you expect the markets
to sort of take away from that.
Abe Sutton: I called out drugs as one of the interest areas
because drugs, you know, biotech and AI, large language models
are probably the two areas where we have the potential for
leveraging advancements in a way that is truly deflationary in
the healthcare system. Things that do not involve one to one
labor input to delivery, but things that are capitalizable.
And so drug advancements could potentially avoid
hospitalizations, could avoid a lot of treatments. Same thing
with getting people engaged in a preventive health activity,
getting people to have support between office visits from AI.
And so I'm excited about both of these areas.
On the drug space, I came into a team that was pretty small
relative to the potential in the innovation center. We had 19
FTEs dedicated to working on drug models. We've now grown
that to 41, and so around double the team. We had one drug model,
that had been designed, negotiated with manufacturers,
the cell and gene therapy model for sickle cell, and coming in,
there was some uncertainty what would happen. And we quickly
were like, oh, this is a really clever design.
This makes sense. It's a win for Medicaid programs. It's a win
for the manufacturers and it will improve quality for these
beneficiaries in line with our statutory mandate. And we leaned
in pretty hard to try and recruit states to say, you
should sign up for this model. And so we had a whole team, the
Medicaid team, the innovation center team calling up states,
and then where necessary, we had the Medicaid, director, the
director of CMCS.
And then, I also got involved in calling different states, were
helpful to try and get them to agree to come in on the model.
We looked at that foundation from the cell and gene therapy
model for sickle cell and said, we should grow that out. And so
the generous model, which is about most favored nation for
drugs in the Medicaid program, actually takes a similar
approach. Manufacturers voluntarily coming in to say,
let's give this different pricing to Medicaid, let's agree
to standardized formulary placement prior authorization
regimens, and then make that price available to states. And
states have the opportunity in generous drug by drug to select,
is this a drug that I should select under generous or is the
rebate structure and the supplemental rebate that I
negotiated already, whether on my own or through a
collaborative, a better deal for me.
And because it's drug by drug, as there's variation between
what states have been able to negotiate, there is a win for
states. If your price is lower than the most favored nation
price, because supplemental rebates don't count in that
calculation, great, just keep that. But if your price is
higher, switch to the MFN price. And so that's just a win for
state budgets and state Medicaid programs that I hope will
improve the sustainability of the Medicaid program and bring
most favored nation pricing to our Medicaid programs and the
most vulnerable beneficiaries on an insurance program in the
country. Then we have the approach for Medicare where
we've laid out two mandatory models, one on Part B and one on
Part D to also utilize most favored nation pricing.
Those are ones that we have proposals out but have not
finalized at this time. And so we are going through the
comments and we appreciate the public's engagement with them.
Finally, we have the balanced model, which as you noted is a
GLP-one coverage model. And while we did take a step to
delay its initiation in Medicare, we are moving forward
with its initiation for Medicaid coverage for GLP-one drugs to
make an affordable price available to Medicaid programs
to the extent that they choose to opt in to that program for
one of the transformative advancements in the
biopharmaceutical space, that we have at this time. Within the
GLP-one space, we are also running a four zero two
demonstration, which is a different type of demonstration
project, called the bridge demonstration, which will run
through the end of twenty seven and give access to Medicare
beneficiaries who have qualifying conditions at the $50
price the president negotiated for these products.
All of that is what we've come out with so far, but I truly
believe that there is more room that we could build on in the
drug space as the innovation center. I think there's a lot
more to be done to make drugs more affordable and encourage
investment in advancements in these spaces. So the cell and
gene therapy model, we should look at expanding it to other
indications. There are other areas where there are not
aligned incentives to prove out the uses for different things,
and we could potentially fix that using the power to reshape
our reimbursement flows. And so I'm really excited about this
space.
I think there's more to do. We just need to recognize that the
signal is going to particular set market segments or
particular payers as we look at, you know, Medicare, Medicaid, B
versus D, you know, generics, brand name, cell and gene
therapies versus other types of advancements. And so we just
need to be aware of the market structure and what we're
targeting in anything that we come out with.
Rob Lott: Say a little more about that awareness. How are
you kind of monitoring, or what are you hearing from
manufacturers and payers? Are they happy to sit down at the
table with you? I mean, you are the behemoth in this space, so I
imagine they're not gonna walk away. But how how are all these
different programs kind of being received by folks on that side
of the table?
Abe Sutton: In general, we have more positive receptions to
voluntary models, than to mandatory models on the part of
those who will be mandated into them. So I'll just acknowledge
that. I also think mandatory models are an incredibly
valuable tool for lowering costs in our healthcare system and
ensuring there's not selection bias in how a model is tested.
And so I think it's an important part of our toolkit. There's a
reason why we proposed three mandatory models last year,
Guard, Globe and our ambulatory specialty model, it's because I
think based off the history of the innovation centers test,
there's some data suggest that they are more likely to be
certifiable than, the voluntary models.
And so that's part of our calculus in fulfilling our
statutory mandate. But I do think in the drug pricing space,
there is room for win wins as the cell and gene therapy model
shows where we can align incentives and have voluntary
participation as well. There are many people who have come to us
with proposals on biosimilar pricing of, can you do this
differently? It really disincentivizes us making
investments here. Or, you know, there is a real pipeline of cell
and gene therapies that the FDA is reviewing.
What does reimbursement look like on the other side? Is there
a smart approach you could design that works for us and
works for these programs? And so people are actually coming to us
with ideas, which is helpful. And we're asking questions,
we're vetting. We also have a number of ideas of our own.
And so that dialogue and input is helpful as we're advancing
our thinking.
Rob Lott: Well, Abe Sutton, thank you so much for taking the
time to chat with us here today. I really appreciate it. Thanks
for joining us here on A Health Podyssey.
Abe Sutton: It's a privilege to get to join. If I could just put
one note in at the end. Yeah. I would love to encourage any of
your listeners who are thinking through the next move in their
careers or next move for graduate students of theirs to
consider sending resumes our way at the Innovation Center. We are
actively hiring and we are looking for folks with the
expertise and knowledge on the drive to reshape our approach to
healthcare and make care more affordable.
Thank you for having me on. It's a privilege to get to join and
speak with you and this audience.
Rob Lott: Absolutely, and to our listeners, thanks for tuning in.
If you enjoyed this episode, leave a review, recommend it to
a friend, and of course tune in next week. Thanks, everyone.
Abe Sutton: Thanks for listening. If you enjoyed
today's episode, I hope you'll tell a friend about A Health
Podyssey.