Why Medicare’s Hospital Wage Index Exceptions Jumped 60%

Health Affairs Publishing’s Rob Lott speaks to Geoffrey Hoffman of the University of Michigan about his recent paper exploring the structure of Medicare’s hospital wage index and discusses the growth of exceptions over time, exploring their implications for how the system functions and whether it meets its intended policy objectives.

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Rob Lott: Medicare is a national program, of course, and it

covers a national population that is therefore broad and

diverse. There are beneficiaries who live in big cities and small

towns. It pays providers delivering care everywhere from

the redwood forests to the Gulf Stream waters. Now the cost of

doing business in one region can differ quite a bit from one part

of the country to the next. And so when the inpatient

perspective payment system was created in 1983, it made sense

to implement a hospital wage index that standardizes Medicare

hospital payments for labor cost differences.

So, otherwise, equivalent hospitals might be paid more

when they operate in areas with higher labor costs than in areas

with lower labor costs. It's a relatively straightforward

adjustment, but what happens when we create a series of

exceptions to that system? Do they achieve their policy goals,

or do they undermine the broader program? And just how broadly

might those exceptions be applied over time? That's our

question on the podcast today.

I'm here with Doctor. Geoffrey Jay Hoffman, an associate

professor at the University of Michigan School of Nursing,

Together with his coauthor Syracuse University's June Lee,

they have a new paper in the June issue of Health Affairs.

Its title is also one of its main findings. Quote, Medicare's

Hospital Wage Index exceptions grew by nearly 60% from 2016 to

2024. It's a really fascinating study on a topic that's not well

understood and definitely not well covered, and we're so

pleased to be publishing it.

I'm looking forward to learning more about it here today.

Doctor. Geoffrey Jay Hoffman, welcome to A Health Podyssey.

Geoffrey Hoffman: Thank you so much for having me. It's a

pleasure and a privilege to be here.

Rob Lott: All right, well, let's dive right in. In my

introduction, I gave a sort of very brief description of the

hospital wage index and how it works. Can you say a little more

about sort of the mechanics and, generally what it does?

Geoffrey Hoffman: It's fairly complex, although the premise is

pretty straightforward. The premise is you wanna make sure

you're adequately reimbursing hospitals for their labor costs.

And, to wit, you wanna standardize by sort of area

labor prices to make sure that if you're in the Bay Area with

really expensive labor prices, you're able to hire the right

skill mix and, offer, you know, postings with the right salaries

so you get the high quality care to deliver to patients, in the

Medicare population. Similarly, if you're in a lower cost area,

you would standardize, to to appropriately reimburse in those

areas. So so far so good.

Obviously, the the the devil's in the details. And so over

time, this program, which started out, sort of with a a

pretty straightforward chassis, if you will, sort of you you

identify the average hourly wages in various labor markets

across the country, and you standardize to a wage index of

one, which would be your right on the nose of the national

average hourly wage, you get a wage index higher than that if

you have a higher average hourly wage in your labor market and so

on and so forth. But over time, it's become, frankly,

increasingly complicated with more, parts and whistles, and

and elements built on top of the initial chassis. So so the the

the good part of that is that, I think the the ensuing

developments as the Wagenyx has grown in complexity have been

well intended. The point purpose was really to address some of

the inherent shortcomings, which are sort of mechanical.

You can't perfectly identify what is the labor market, how

exactly the commuting patterns work, and what is the price of

labor versus sort of hospital decisions about the types of

wages they want to offer for reputational or other reasons.

Policymakers have thought about the types of data that have gone

into the wage index. But over time, there have been a number

of exceptions that have evolved. But but the basic premise is

pretty straightforward. As I say, hospitals supply their

their wages.

CMS uses that to compute these wage indices, and then they're

applied to, each hospital's base operating payment, if you will.

Rob Lott: Great. Well, you said the devil's in the details.

Let's, let's face this devil, straight on. You alluded to

these exceptions, and I'm wondering if you can say a

little more about how the exceptions to the index are

established, and maybe you can give us an example of how it

might affect a hospital's reimbursement.

Geoffrey Hoffman: So, I mean, there are several types of

exceptions, and some of them involve this idea of

reclassification. Others involve something called the rural floor

and the low wage adjustment. There's a frontier adjustment.

There's a there's a a handful of them. Reclassification is maybe

the easiest place to start.

The Wage Index basically uses CMS's definition of a rural or

urban hospital to start, which are based on statistical area

criteria. So are you in a metropolitan area or not? All

hospitals that are in an MSA or metropolitan area each have

their own wage index that's assigned to them that's

empirically computed. For all the other hospitals that are not

in metropolitan areas, those are considered rural hospitals under

the wage index criteria set forth by CMS. And they whether

they're all contiguous or not, any hospital in a rural area is

assigned the same wage wage index.

So what ends up happening is you you have these neighboring labor

market issues where hospitals will assert that, yes, I'm on

this side of the of the labor market, you know, divide, but we

pull from the same labor market as this other hospital. We're

actually facing higher prices. The ones that are assigned to us

empirically are not appropriate. So there is a mechanism for

hospitals to apply to reclassify so they can do one of three

things. They can, within MSAs, move to, for the purposes of the

wage index, a separate MSA.

Rob Lott: Presumably one with higher a higher reimbursement.

With a higher reimbursement.

Geoffrey Hoffman: So it's based on geographical proximity and

then relative wages. So there are certain criteria you have to

meet. But, essentially, if you're in Riverside County and

you're adjacent to Los Angeles County and you qualify under

these criteria, you can apply to the reclassification board, and

they will grant you this exception or reclassification

for a three year period. Other types of reclassification can be

actually from an urban to the statewide rural area or vice

versa. And there are technicalities and benefits to

each of these for hospitals and why they do them and why some

are growing in prevalence over time that are kind of

interesting.

So, essentially, if you just think of, the San Jose or not

San Jose, but the, Riverside County to Los Angeles MSA,

reclassification, what could end up happening is you reclassify,

and then your wage index increases. Let's say it

increases by about, 10%. The pass through from the wage index

to the actual payment that comes from CMS is about 60 to 70%. So,

essentially, the way that works mechanically is that the the

wage index is multiplied by the labor related portion of the

base operating payment. So depending on whether the wage

index is above or below the national average, that can range

from a 62%, the labor portion of the base payment, or up to 69%,

but those vary by year.

So, basically, if you get a 10% increase through this

reclassification, every single payment you get from Medicare,

you get a 6% to 7% increase in your reimbursement.

Rob Lott: Okay. Well, let's dive right into your paper then sort

of with that background. You conducted a descriptive trend

analysis of those exceptions from fiscal year twenty sixteen

through fiscal year twenty twenty four. Give us some of

your top line findings.

Geoffrey Hoffman: What we found looking over this nine year

period is essentially boils down to pretty large growth in the

use of exceptions over time in this hospital wage index policy.

The growth was primarily driven by several of these various

exceptions that I, referenced a few minutes ago. The main one

is, what's called the low wage adjustment, which was introduced

in fiscal year twenty twenty and then actually expired in fiscal

year twenty twenty four due to some court cases. And that, by

definition applied to 25%, the lowest, quartile of hospitals in

terms of their annual wage index. So that applied to if

there's some 6,500 IPPS hospitals that are subject to

the wagener next, it's a quarter of those.

And then you add really large growth in what's called the

rural floor policy as well. And the rural floor is a separate

exception where if you are in an urban area for the purposes of

the wage index, you, by definition, need to have a wage

index value that's higher than the statewide rural wage index.

And so there was really large growth in that exception as

well. So, basically, we went from less than half of hospitals

getting an exception to this policy back when our study

period started up to seven in 10 hospitals getting a wage and

exception now. So, basically, like, if you think that you've

developed a really good program for standardizing payments, but

seven out of 10 of, the hospitals this policy applies to

are getting exception, maybe, yeah, something's

Rob Lott: not working. You initially thought. Yeah. Yeah.

Well, I wanna ask you a little more about, those details.

But first, let's take a quick break. And we're back. I'm here

with doctor Jeffrey Jay Hoffman from the University of Michigan

talking about, his new paper in the June issue of Health Affairs

about Medicare's hospital wage index exceptions. You started

looking at these exceptions. Fewer than half of hospitals

were taking them when you started your study period, and

now it's about seven and ten.

I'm curious if you have a sense of what's behind that growth. Is

it that there are more exceptions, or is it that more

hospitals are seeing these exceptions as a avenue to

increasing their revenue?

Geoffrey Hoffman: Yeah. It's a great question. And, you know,

I'm not sure with this study we can provide any definitive

answers. I think based on sort of intuition from studying this

program, it's probably a combination of the two. I think

one part of the growth is just purely mechanical, the

introduction of this low wage this was a policy that was

designed with the intent of helping the hospitals that tend

to have low wage indices over time get out of what is in the

policy vernacular called the circularity trap.

Essentially, like, once you're down at the bottom, you're

really gonna have a hard time digging out. And there's several

reasons for that. One is as you continue to get lower payments,

you can't then hire more costly labor to sort of get out of that

trap. And secondly, there's a huge lag between the actual

wages paid and when those data are incorporated into the wage

index calculation, so four year lag. So even if you somehow

manage to start increasing wages, you're still gonna be

stuck with the low wage index values over time for a while.

So the low wage, adjustment was sort of intended to address this

phenomenon, you know, just sort of by by fiat, you know, if

you're in that low wage group, after all the other exceptions

are applied, like, you're sort of straggling, hey. We're gonna

we're gonna, basically give you an adjustment relative to that

twenty fifth percentile value. We're gonna if you're, like,

down at the tenth percentile, we're gonna give you half the

difference essentially. So a lot of hospitals benefited from

that. And so as that disappeared at the end of our study period,

you will see a decrease, in wage and next exceptions.

That said, there's still growth in other areas that's noteworthy

and we think is worth some policy attention. The rural

floor in particular grew a lot. And, again, to sort of re

restate the the the issue here. So rural floor is all urban

hospitals in a given state are mandated by statute to have a

wage index that's at least as high or higher than all the

hospitals in the statewide rural areas. So as an example of this

historically that policymakers have pointed out, as

problematic, in Massachusetts, a critical access hospital back

in, I believe, 2008 converted to rural hospital status.

And because it was on Nantucket Island where it had high wages,

And because of that, all the urban hospitals in the state at

that time were subject to the rural floor, meaning their wage

indices increased, not through any sort of labor market

phenomenon that was materially affecting the prices they faced,

but simply due to this sort of, you know, reclassification

phenomenon, of a smaller hospital. And so the rural

floor, because of that, was recognized as being gameable.

So, basically, you could be an urban hospital reclass and

higher wage and reclassify as rural, particularly if you're a

chain hospital, then the other hospitals in the chain and other

hospitals can get this bump. So over time, you've had more and

more hospitals, obtaining the roll floor adjustment, which has

been really good for them. And but the rural floor is being set

by fewer and fewer hospitals, so you're sort of getting this

weird asymmetry.

So what CMS did back in 2020 was they basically said, we're gonna

try to get ahead of this gaming. If you're an urban hospital and

reclassify as rural, We're not gonna include your reclassified

hospital's wages into the rural floor calculation to sort of

subvert this opportunity of gameability, if you will. But

that too, like the low wage adjustment, was struck down by

courts. And so CMS flipped the script in 2024 and sort of

reopened what has been characterized as that loophole

by MedPack. And so I think we're seeing growth, again in the

exceptions due to that phenomenon.

But this study didn't particularly study that using

sort of the econometric methods, so this is more conjecture based

on the descriptive findings.

Rob Lott: Well, so as these exceptions grow, I presume it

sort of makes the problem harder and harder to fix. This is sort

of like the struggle of putting the toothpaste back in the tube

that sort of people kind of start to build their business

models around these exceptions and then it makes it a lot

harder to undo them. Are there steps that policymakers can take

in this space to ensure that the exceptions are actually

achieving a worthwhile policy goal?

Geoffrey Hoffman: Yeah, I think so. And just briefly before I

jump into that, I wanna just state sort of the scope of this.

So we've been talking about the hospital wage index, which

obviously applies to hospitals, but the hospital wage index is

also applied to outpatient, the outpatient payment system, the

SNF, skilled nursing facility payment system, hospice, and

home health. So it's a really sort of broader phenomenon. Now

the exceptions component is not included in the skilled nursing

facility wage index, but this sort of issue carries through,

into a lot of other sectors.

In terms of potential fixes, people have been studying this

going way back to, early two thousands. CMS contractors have

written reports on it. MEDDPAC has periodically written

reports. There are a lot of proposals out there. I think

MEDDPAC and, the National Academies of Science and

Medicine have proposed as going as far as just eliminating the

all exceptions to the wage index and sort of starting from

scratch, if you will.

But there are other, potential fixes that I think there's some

consensus on across, across policymakers. So, one big fix is

getting rid of hospitals reporting their own wage data.

So there's sort of this endogeneity problem there.

Endogeneity just meaning, like, you're reporting your own wages.

There's opportunities for obviously, if you report higher

wages, you get more.

So how specific hospitals sort of classify contract and other

types of labor and figuring that out, you could sort of address

that issue through using a separate wage data source. The

other problem with the data is that we use hospital specific

data where the prices you face in the labor market may be

broader than just hospitals. You're competing with skilled

nursing facilities, home health agencies for nursing labor. So,

there's been many proposals to use the Bureau of Labor

Statistics data, which have occupation industry wide data

and sort of weight weight those wage data by occupation. MedPAC

also has, discussed, trying to address the phenomenon of where

you, like, have statewide rural areas that encompass hospitals

in rural areas that, like, in Alaska might be thousands of

miles apart, but also within MSAs, which can be very large,

like the Los Angeles one, and looking at variation within

them, gets at, sort of the potentially the desire of

hospitals to reclassify if there's already recognition

that, you know, in Ronald Reagan Hospital in Westwood is gonna

have a different labor price than something abutting

Riverside County over there.

I'm going to LA County because I used to live there. Anyway, and

then finally, there's the sort of cliff issue, which is, like,

if you're just on one side of the market versus another,

trying to smooth those borders so it's not such a cliff, which

could also mute the desire incentives of hospital to try to

game or in or reclassify. Others have contended that once you go

down that route, you're blending and blending and blending. And

then those that don't blend, then you're still getting

distortions in the sort of true, prices of hospitals. But there

are certainly ways to get away from the distortions in the

current setup.

Rob Lott: So before we wrap up, a few thoughts from you perhaps

on other opportunities for research in this area. As you

said, you sort of made some conjectures about what was

driving some of these, some of this growth. Where would you

like to turn next as a researcher in this space?

Geoffrey Hoffman: Yeah. There's a lot of opportunities for

growth in this space. As you know, there have not been a lot

of sort of peer reviewed studies in this area, which I think is,

you know, unfortunate given the sort of payment implications.

There's a couple of avenues. One sort of easy place to start are

just the various exceptions and just sort of seeing a what are

the effects of obtaining or losing some of these exceptions

just in terms of financial implications?

The second is, does the Wage Index actually sort of obtain

its policy goal of helping hospitals hire higher wage and

higher skill mix labor. So that's an outstanding question.

Do they just use additional dollars for sort of

administrative costs or other things? I don't think we know

much about that. A sort of broader, more extensive look

could look at does this over time if these are really big

financial, payments.

Does it particularly if they're empirically un unmerited, do

these lead to improvements or harms in patient health, things

of that nature? And I think just going further than that, you

could think about, you know, the wage index also affects sort of,

Medicare Advantage. There's a small component of Medicare

Advantage payments, sort of what happened during COVID and other

phenomenon that that affected the labor market, and wages. So

lots to look at that, is an exciting area of research.

Rob Lott: Indeed. Well, lots to look at and lots to, investigate

down the road. Doctor Jeffrey Jay Hoffman, thank you so much

for taking the time to join us here on A Health Podyssey.

Geoffrey Hoffman: Thank you so much, Rob. It was a pleasure.

Rob Lott: To our listeners, thanks for tuning in. If you

enjoyed this episode, please leave a review, recommend it to

a friend, and of course tune in next week. Thanks, everyone.

Thanks for listening. If you enjoyed today's episode, I hope

you'll tell a friend about A Health Podyssey.