Research Issues and the Designs

Strategies and Quantitative Studies 

https://doi.org/10.1177/1077558717744611

Medical Care Research and Review
2019, Vol. 76(5) 643 –660

© The Author(s) 201

7

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DOI: 10.1177/1077558717744611

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Empirical Researc

h

Hospital Readmissions
Reduction Program:
Intended and Unintended
Effects

Min Chen1 and David C. Grabowski2

Abstract
This study examines whether the Hospital Readmissions Reduction Program (HRRP),
which penalizes hospitals with excess readmissions for certain conditions, has reduced
hospital readmissions and led to unintended consequences. Our analyses of Florida
hospital administrative data between 2008 and 2014 find that the HRRP resulted in
a reduction in the likelihood of readmissions by 1% to 2% for traditional Medicare
(TM) beneficiaries with heart failure, pneumonia, or chronic obstructive pulmonary
disease. Readmission rates for Medicare Advantage (MA) beneficiaries and privately
insured patients with heart attack and heart failure decreased even more than TM
patients with the same target condition (e.g., for heart attack, the likelihood for TM
beneficiaries to be remitted is 2.2% higher than MA beneficiaries and 2.3% higher
than privately insured patients). We do not find any evidence of cost-shifting, delayed
readmission, or selection on discharge disposition or patient income. However,
the HRRP reduced the likelihood of Hispanic patients with target conditions being
admitted by 2% to 4%

.

Keywords
Medicare, readmissions, hospital, discharg

e

This article, submitted to Medical Care Research and Review on 30 June 2017, was revised and accepted
for publication on November 6, 2017.

1Florida International University, Miami, FL, USA
2Harvard Medical School, Boston, MA, USA

Corresponding Author:
Min Chen, College of Business, Florida International University, 11200 SW 8th Street,
Miami, FL 33199, USA.
Email: min.chen2@fiu.edu

744611MCRXXX10.1177/1077558717744611Medical Care Research and ReviewChen and Grabowski
research-article2017

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mailto:min.chen2@fiu.edu

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644 Medical Care Research and Review 76(5

)

Introduction

Hospital readmissions are common and costly. In 2011, the U.S. Medicare program
paid for

1.

8 million 30-day readmissions with a total cost of $24 billion (Hines, Barrett,
Marguerit, Jiang, Joanna, & Steiner, 2014). Some readmissions could be prevented
with better quality of care (Axon & Williams, 2011), and the Medicare Payment
Advisory Commission (MedPAC) estimates that a 10% reduction in avoidable read-
missions would save the Medicare program at least $1 billion (MedPAC, 2013). To
achieve both better outcomes for patients and greater savings for Medicare, the
Affordable Care Act (ACA) created the Hospital Readmissions Reduction Program
(HRRP), which applies financial penalties to acute care hospitals with higher-than-
expected readmission rates among Medicare fee-for-service (FFS) beneficiaries in the
30-days following discharge for certain target conditions.

Since October 2012, the HRRP has targeted three conditions: acute myocardial
infarction (AMI), congestive heart failure, and pneumonia. Beginning in October
2014, total hip or knee replacement and chronic obstructive pulmonary disease
(COPD) were also included in the program. The Centers for Medicare and Medicaid
Services (CMS) calculates the average risk-adjusted, 30-day hospital-readmission
rates for patients with each targeted condition and penalizes hospitals that perform
worse than the national average. For Fiscal Year (FY) 2013, the maximum penalty for
a hospital with excess readmissions was 1% of its total Medicare base payment. The
penalty went up to 2% of the Medicare base payment for FY 2014, and 3% for FY
2015 forward (CMS, 2016).

New Contribution

Prior studies have examined the initial three target conditions (i.e., AMI, heart failure,
and pneumonia) and suggested that the HRRP has lowered 30-day readmissions
among Medicare FFS beneficiaries (Carey & Lin, 2015; Gerhardt et al., 2013;
Zuckerman, Sheingold, Orav, Ruhter, & Epstein, 2016). Using Medicare FFS claims
data, two recent articles compared the changes in readmission rates by hospital penalty
status and confirmed that hospitals with the lowest pre-HRRP performance had the
greatest improvement (Desai et al., 2016; Wasfy et al., 2017). How readmissions
change among Medicare Advantage beneficiaries and privately insured patients, how-
ever, is still somewhat unclear and vitally important. Because the HRRP penalties only
apply to traditional Medicare patients, one way that a hospital could recoup lost
Medicare reimbursements as a result of excess readmissions would be to readmit more
privately insured or Medicare Advantage patients. In this study, we exploit a state-
based all-payer dataset (through 2014) to examine the overall impact of the HRRP on
readmissions among traditional Medicare, Medicare Advantage, and privately insured
patients, respectively. We examine not only the aforementioned three originally tar-
geted conditions but also the two new penalty conditions (i.e., COPD and total hip or
knee replacement).

Furthermore, we explore several other potential consequences of the HRRP across
targeted and nontargeted conditions. First, we examine the impact of the HRRP on

Chen and Grabowski 645

readmissions post–30 days to detect if the HRRP has simply delayed readmissions.
Next, we examine whether the HRRP led to any “cherry picking” of low-risk patients
at admission. Finally, we examine whether the HRRP led to increased skilled nursing
facility (SNF) or home health agency (HHA) discharges.

Conceptual Framework

The HRRP is a very direct policy instrument. Hospitals are financially penalized for
excess 30-day readmissions for the target conditions. Medicare’s goal in implementing
the HRRP was to encourage hospitals to reduce 30-day readmissions through better
hospital care. In response to the HRRP, we hypothesize that hospitals will lower read-
missions for these target conditions assuming the cost of reducing readmissions is
below the amount of the readmission penalty. We also assume that hospitals want to
avoid any negative reputation effects associated with being penalized (Winborn,
Alencherril, & Pagán, 2014), which might lead them to lower readmissions even if the
cost of doing so exceeds the readmission penalty.

Because the HRRP is a relatively blunt policy, we expect it to incent hospitals to
change their behaviors in both intended and unintended ways. In terms of unintended
consequences, strong potential exists for what economists term the multitasking prob-
lem in which providers direct their efforts toward those metrics for which they might
be penalized while shirking on those metrics for which they are not penalized. Under
the HRRP, hospitals would have the incentive to push any readmissions out past day
30 when they are no longer penalized for the readmission. Critics have suggested that
hospitals might dodge the HRRP penalties by increasingly placing returning patients
within 30 days of discharge on observation status (Himmelstein & Woolhandler, 2015;
Noel-Miller & Lind, 2015). Observation stays are billed as outpatient services rather
than readmissions to acute care and would not be counted in the HRRP penalty calcu-
lation. Between 2006 and 2013, observation stays increased by 96% for Medicare
patients (MedPAC, 2015). One recent study, however, did not find a statistically
significant increase in observation stays for targeted versus nontargeted conditions
(Zuckerman et al., 2016).

Another unintended consequence would be to discharge patients with a low risk of
readmission to costlier postacute care settings because the hospitals are only at risk for
readmissions under the HRRP and not postdischarge spending. Thus, at the margin,
hospitals have the incentive to increase discharges to home health and skilled nursing
facilities for the HRRP target conditions if such discharges would help hospitals reduce
readmission rates. From Medicare’s perspective, spending on these postacute services
would likely more than offset any potential savings from decreased 30-day
readmissions.

Finally, the HRRP’s readmission measures adjust for demographic characteristics
associated with higher rates of hospital readmissions (such as age) and severity.
However, they do not allow risk adjustment based on patients’ race, ethnicity, or socio-
economic status. Because patients with low socioeconomic status are found to have
higher readmission rates than the overall population (Hu, Gonsahn, & Nerenz, 2014),

646 Medical Care Research and Review 76(5)

hospitals may respond to the omission of these risk factors by selecting patients on
race and socioeconomic status associated with lower rates of hospital readmissions.

Method

Data and Outcome Variables

We construct our hospital admissions and readmissions measures using the State
Inpatient Discharge data, collected and maintained by the Florida Agency for Health
Care Administration. The data contain detailed information on all inpatient stays in
Florida from Quarter 1 of 2008 to Quarter 4 of 2014 and a unique patient identifier that
allows us to track a patient’s historical visits across hospitals over time. In addition, we
used Medicare Hospital Compare data released in July 2009 (for the period July 2005–
June 2008) to examine baseline risk-adjusted readmission rates at the inpatient pro-
spective payment system (IPPS) hospitals in the United States.

We adapt methods used in the prior studies to construct index hospitalization and
30-day all-cause readmission at the patient level. Specifically, we code index hospital-
izations as stays in which no inpatient discharge had occurred within the previous 30
days. Hence, a hospitalization is either an index stay or a readmission. We then iden-
tify target conditions by the principal diagnosis or procedure of the index hospitaliza-
tion, using Healthcare Cost and Utilization Project’s (HCUP’s) Clinical Classifications
Software (CCS). CCS is a tool that collapses diagnosis and procedure codes from the
International Classification of Diseases, 9th Revision, Clinical Modification
(ICD-9-CM).1 We used the single level CCS diagnosis code 100 for AMI, 108 for
heart failure, 122 for pneumonia, and 127 for COPD. The CCS procedure code used
for total hip and knee replacement is 152-153. The ICD-9 codes used to identify total
hip and knee replacement are 81.51 (primary hip replacement) and 81.54 (primary
knee replacement). In addition, we follow the prior literature (Carey & Lin, 2015;
Mellor, Daly, & Smith, 2016) and select gastrointestinal conditions with Medicare
Severity Diagnosis Related Group (MS-DRG) codes 329-331, 377-379, and 391-392
to be our control group of Medicare index hospitalizations.2

Similarly, we define two additional indicator variables when readmission occurred
within 45 days or 60 days, respectively, and compared them to the 30-day readmission
to identify if readmission occurred within 31 to 45 days or 31 to 60 days. Finally, we
use the disposition of the patient at discharge to code dummy variables indicating
whether the patient was discharged to an SNF or HHA.

Control Variables

To control for heterogeneity associated with changes in readmission and other out-
comes over time, our models include a rich set of patient-level covariates. The covari-
ates include demographics such as sex, age group, race, primary payer, income
category, and rural/urban location. We also constructed time-varying clinical measures
for severity adjustment, including (1) indicators of high severity with major

Chen and Grabowski 647

complications/comorbidities based on the MS-DRG codes and (2) the number of
comorbid conditions compiled from a set of 29 binary variables identifying coexisting
medical conditions that are not directly related to the main reason for index admission
(refer to HCUP’s Elixhauser Comorbidity Software for details).3

We identify and exclude certain index hospitalizations following the rules specified
in the technical reports of constructing 30-day all cause readmission rates prepared for
CMS: (1) hospitalizations during which patients died, (2) discharged against medical
advice, and (3) discharged or transferred to another acute care facility. For AMI admis-
sions, we also excluded cases with same-day discharges. The analysis sample contains
951,215 index admissions from 156 hospitals.

Statistical Analysis

We use a difference-in-differences (DD) method to compare changes in outcomes of
patients in the treatment group before and after the HRRP relative to changes in out-
comes of the control group. The treatment group consists of Medicare FFS beneficia-
ries aged at least 65 years old and with one of the five HRRP target conditions as the
primary diagnosis for their index admission. For each condition we use three different
comparison groups for a total of 15 models. The first comparison group consists of
hospital admissions among Medicare FFS patients aged 65 years and older and with
gastrointestinal conditions as their primary diagnosis. The second comparison group
includes hospitalizations of each of the five target conditions among Medicare
Advantage patients aged 65 years and older. The third comparison group comprises
privately insured patients with those five target conditions.

We estimate the following model:

Y Post Treatment Post

Treatment X Hospi
iht t i t

i it

=

+

+ + +

∗ +α µ µ

µ β
1 2

3 � ttalh iht+ε
(1)

where Yiht is an indicator for a study outcome for patient i at hospital h in time period
t. More specifically, we first examine if the patient was readmitted within 30 days of
discharge and if there is any delayed readmission after 30 days but within 45 or 60
days of discharge. We also examine if the patient was discharged to a costlier postacute
care setting (i.e., a SNF or a HHA). Finally, we examine whether the HRRP reduced
the likelihood of admitting minority patients or lower income patients. Minority
patients are indicated by whether the patient is Black or of Hispanic ethnicity. We iden-
tify a patient to be in a lower income region if the patient resides in a ZIP code wherein
the estimated annual median household income is in the bottom two quartiles. Each of
these outcome measures represents a separate regression.

Postt is a dummy variable set to 1 if the observation is from the posttreatment
period in either the treatment or a comparison group. We use 2008-2009 as the pre-
HRRP period and 2012-2014 as the post-HRRP period for AMI, heart failure, and
pneumonia. For the two newly added conditions (i.e., COPD and total hip or knee
replacement), we use 2014 as the post-HRRP period. Treatmenti indicates whether the

648 Medical Care Research and Review 76(5)

index admission was a hospitalization targeted by the HRRP, and equals zero if the
index admission was part of a comparison group. The interaction effect of Postt *
Targeti represents our key variable of interest, the DD estimate of the impact of the
HRRP. Xit is a vector that captures the time-varying patient characteristics (listed in
Table 1). The hospital fixed effects (Hospitalh) are used to control for the unobserved,
time-invariant differences across hospitals.

Thus, we use pre-HRRP levels for the target admissions and concurrent changes
from the precontract to postcontract period in the nontarget admissions to establish
counterfactuals that would be expected in the absence of HRRP program, and we esti-
mate changes that differed from this expectation (i.e., the differential change or the
change attributable to the HRRP). For all the regression analyses, the standard errors
are clustered at the level of the hospital to allow for an arbitrary covariance matrix
within the clusters.

Because penalties are based on whether a hospital’s readmission rate exceeds the
national average, hospitals with a baseline readmission rate above the threshold are at
greater risk of the penalty and thus have stronger incentives to improve. In July 2009,
the CMS Hospital Compare website began to publicly report IPPS hospitals’ perfor-
mance in 30-day readmission rates for AMI, heart failure, and pneumonia, respectively.
For each IPPS hospital with more than 25 cases, its performance is classified into three
categories: “better than U.S. national rate,” “no different than U.S. National Rate,” or
“worse than U.S. national rate.” We use the national rate for the period July 2005 to
June 2008 obtained from CMS’s Hospital Compare data as the baseline threshold rate
and compare the hospital specific average 30-day readmission rates to the national
average to define if a hospital is “at risk” for any penalty.4 Given that penalties are
based on a hospital’s past 3-year average readmission performance, partial responses
might be observed immediately after ACA passage but before penalties go into effect.
Using historic readmission rates prior to ACA passage allows us to test the full effects
of the HRRP. To examine how the impact of HRRP varies across hospitals with differ-
ent risks of facing the penalty, we divide the sample into two groups based on whether
patients were admitted into a hospital with its baseline readmission rate above the
threshold rate, and then we re-estimate the DD model on both of the subsamples.

We further compare this DD estimate of patients treated at hospitals at risk for
HRRP penalties versus those patients treated at hospitals not at risk for penalties. More
formally, we estimate the triple difference model (DDD) specified below:

Y Post Target Risk Post Target Post

Risk
iht t i h t i t

h

= + + +

+

∗ ∗

∗

∗

α µ µθ 1 2
µµ γ

γ γ β
3 1

2 3

Target Risk Post

Target Risk X Year
i h t

i h it t iht

∗ +

+ + + + +ε

(2)

Compared with Equation (1), the added variable Riskh is an indicator variable that
specifies whether a hospital is at risk for HRRP penalties, which equals to 1 if hospital
h’s baseline readmission rate is above the national average and 0 otherwise. The inter-
action effect of Postt * Targeti * Riskh represents our key variable of interest, the triple
difference estimate of the impact of the HRRP.

649

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0.
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56
[

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79

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55
[

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83
[

1.
77

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2.
22
[

1.
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ig

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N
95

1,
21

5
10

6,
84

4
16

3,
37

8
13

6,
61

9
81

,6
22

15
0,

33
5

31
2,

41
7

N
ot

e.
A

M

I
=

a
cu

te
m

yo
ca

rd
ia

l i
nf

ar
ct

io
n;

H
F

=
h

ea
rt

f
ai

lu
re

; P
N

=
p

ne
um

o
ni

a;
C

O
PD

=
c

hr
o
ni

c
o
bs

tr
uc

ti
ve

p
ul

m
o
na

ry
d

is
ea

se
; H

IP
=

t
o
ta

l h
ip

o
r

kn
ee

a
ng

io
pl

as
ty

/r
ep

la
ce

m
en

t;

G
I
=

g
as

tr
o
in

te
st

in
al

c
o
nd

it
io

ns
. M

ed
ic

ar
e

FF
S

re
fe

rs
t

o
M

ed
ic
ar
e

fe
e-

fo
r-

se
rv

ic
e

be
ne

fic
ia

ri
es

. S
ta

nd
ar

d
de

vi
at

i

o
ns

a
re

in
b

ra
ck

et
s.

650 Medical Care Research and Review 76(5)

As noted above, the DDD approach implicitly assumes that hospitals at-risk and
not at-risk for the HRRP share the same readmission shocks in a given hospital and
year that are unrelated to the HRRP policy. The DD approach, which instead used
as controls the within-hospital readmission shocks among patients not included in
the HRRP program, may actually be preferable. Because little basis exists for distin-
guishing these approaches ex ante, these models are probably best viewed as com-
plementary approaches for exploring the validity of this study’s key results.

We conduct additional analyses to explore potential sources of bias. We compare
trends in each outcome between the targeted and nontargeted admissions during the
pre-HRRP period. Similar pre-HRRP trends would support our assumption that
changes from the pre-HRRP to post-HRRP periods would have been similar for the
target and nontarget conditions in the absence of the HRRP program. Considering that
CMS began publicly reporting hospital performance in July 2009 and hospitals might
start to respond by changing their behavior since then, we restrict the pre-HRRP period
to be the first two quarters of 2009 and reestimated all the specifications using the
alternative sample and the results stay robust.

Results

We observe several notable trends when examining the 30-day all cause readmis-
sions by condition from 2008 to 2014 (see Figure 1). First, the 30-day readmission
rates of FFS patients followed similar trends from 2008 to 2009 across the five target
conditions and gastrointestinal condition. Second, the 30-day readmission rates of
FFS patients with each of the five target conditions decreased or stayed relatively
stable from 2012 to 2014, while the FFS patients with gastrointestinal conditions

Figure 1. Thirty-day all-cause readmission trend by condition.
Note. HF = heart failure; PN = pneumonia; HIP = total hip or knee replacement; GI = gastrointestinal
conditions.

Chen and Grabowski 651

experienced an increase in their 30-day readmission rate during the same time
period. Finally, within the same condition, the 30-day readmission rates of FFS and
Medicare Advantage patients followed similar trends from 2008 to 2009. We there-
fore use 2008-2009 as the pre-HRRP comparison period. When comparing across
payers for a given target condition, we also observe that Medicare readmissions
rates were consistently higher than the rates for privately insured patients. Table 1
reports the descriptive statistics of the whole sample and by each of the five target
conditions as well as the gastrointestinal condition. Compared with the national
average, our sample has slightly higher 30-day all-cause readmission rates in AMI
and heart failure and comparable readmission rates in pneumonia, COPD, and total
hip or knee replacement.

We next examine the DD estimates on HRRP targeted admissions using three dif-
ferent comparison groups (see Table 2). Compared with Medicare FFS patients with
gastrointestinal conditions as the primary diagnosis, there was a 1% to 2% decrease in
30-day readmissions for comparable heart failure, pneumonia, and COPD patients.
However, when compared with Medicare Advantage patients with the same target
condition, we observe a statistically significant increase in 30-day Medicare FFS read-
mission for AMI, heart failure, and pneumonia. Similarly, when compared with
privately insured patients, 30-day readmissions for Medicare FFS patients admitted
with AMI and heart failure increased. The results reveal that although the HRRP tar-
geted Medicare FFS patients only, hospital readmission rates declined substantially in
the MA and privately insured population after the HRRP, especially among cardiac
related admissions. This may suggest that there are spillover effects from the HRRP
extending to MA and privately insured patients. We then restrict our attention to MA
and privately insured patients admitted with one of the five HRRP target conditions
and compare changes in their readmissions to those of MA and privately insured

Table 2. Difference-in-Differences (DID) Estimates of the Effect of the Hospital
Readmissions Reduction Program on Medicare FFS 30-Day Readmissions.

Medicare FFS patients
with GI conditions as

control

Medicare FFS with
Medicare Advantage,

same

condition

as control

Medicare FFS with
private insurance, same

condition as control

DID impact

DID impact DID impact

(1) (2) (3)

(1) Heart attack −0.004 (0.005) 0.022*** (0.008) 0.023*** (0.007)
(2) Heart failure −0.007** (0.004) 0.012** (0.005) 0.020*** (0.008)
(3) Pneumonia −0.006* (0.003) 0.010* (0.006) 0.007 (0.005)
(4) Chronic obstructive

pulmonary disease
−0.018** (0.005) 0.005 (0.007) −0.006 (0.008)

(5) Total hip or knee
angioplasty

0.013 (0.008) 0.01 (0.011) 0.002 (0.007)

Note. FFS = fee-for-service; GI = gastrointestinal. All models include control variables listed in Table 1 as well as hospital
fixed effects. The standard errors are clustered at hospital level. Robust standard errors in parentheses.
*p < .1. **p < .05. ***p < .01.

652 Medical Care Research and Review 76(5)

patients with gastrointestinal conditions, respectively. The DD estimates reported in
Appendix Table A1 confirmed that after passage of the HRRP, hospitals reduced car-
diac-related readmissions not only for Medicare FFS patients, but also for MA and
privately insured patients.

Next, we reran the DD estimation conditional on hospitals’ baseline readmission
performance (see Table 3). Compared with admissions with gastrointestinal condi-
tions, index hospitalizations with target conditions at a hospital “at risk” for penalties
had statistically significant lower 30-day readmissions.5 On the contrary, none of the
five target conditions show significant reductions in 30-day readmissions at hospitals
with baseline performance better than the national average and thus at less risk for the
penalties. These findings suggest that the HRRP has been effective in improving the
regulated quality dimensions of the low performers, but high performers at baseline
lacked incentives to further reduce their readmissions.

Having examined the main effect on readmission rates using various comparison
groups, we turn to other potential intended and unintended consequences of the
program. We examine the HRRP on three different dimensions: post–30-day readmis-
sions, discharge status, and potential patient selection on income and race (see Table 4).
Because privately insured patients and Medicare beneficiaries differ in age and other
characteristics that may confound the results, we focus on using Medicare FFS benefi-
ciaries with gastrointestinal conditions and MA beneficiaries with the same target con-
dition as controls. We found no strategic responses from hospitals in terms of
postponing readmissions past 30 days. This is consistent with early findings using data
up to year 2012 (Carey & Lin, 2015) and extends results from another study that

Table 3. Difference-in-Differences (DID) Estimates of the Effect of the Hospital
Readmissions Reduction Program on 30-Day Readmissions Conditional on Initial
Performance at Baseline.

Discharged from hospitals
at risk for penalties

Discharged from hospitals
not at risk for penalties

DID impact DID impact

(2) (4)

(1) Heart attack −0.016** (0.008) −0.006 (0.005)
(2) Heart failure −0.009** (0.004) −0.004 (0.004)
(3) Pneumonia −0.012** (0.005) −0.0004 (0.004)
(4) Chronic obstructive

pulmonary disease
−0.010* (0.005) −0.005 (0.006)

(5) Total hip or knee
angioplasty

0.003 (0.009) 0.0006 (0.006)

Note. The comparison group consists of Medicare fee-for-service patients with gastrointestinal conditions
as their primary diagnosis during index admissions. All models include control variables listed in Table 1 as
well as hospital fixed effects. The standard errors are clustered at hospital level. Robust standard errors
in parentheses.
*p < .1. **p < .05. ***p < .01.

653

T
a
b

le
4

.
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ed

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o
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am
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o
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el

ay
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is

ch
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el
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(1

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ea
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12

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(

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)

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57

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)
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3)

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is
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)
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02

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(0

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)
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)
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)
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(5
)

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el

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01
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N
ot
e.
A

M
I
=

a
cu
te
m
yo
ca
rd
ia
l i
nf
ar
ct
io
n;
H
F
=
h
ea
rt
f
ai
lu
re
; P
N
=
p
ne
um
o
ni
a;
C
O
PD
=
c
hr
o
ni
c
o
bs
tr
uc
ti
ve
p
ul
m
o
na
ry
d
is
ea
se
; H

ip
/K

ne
e

=
t

o
ta
l h
ip
o
r
kn
ee
a
ng
io
pl
as
ty

/
re

pl
ac

em
en

t;
G

I
=
g
as
tr
o
in
te
st
in
al
c
o
nd
it
io

ns
; F

FS
=

f
ee

-f
o
r-

se
rv

ic
e.

A
ll

m
o
de

ls
in

cl
ud

e
co
nt
ro

l v
ar

ia
bl

es
li

st
ed

in
T

ab
le

1
a

s
w

el
l a

s
ye

ar
a

nd
h

o
sp
it
al

f
ix

ed
e

ffe
ct

s.
T

he
r

o
bu

st

st
an

da
rd

e
rr

o
rs

r
ep

o
rt

ed
in

p
ar

en
th

es
es

a
re

c
lu

st
er

ed
a

t
ho

sp
it
al

le
ve

l

.
*p

< .1

. *
*p

< .0

5.
*

**
p

< .0 1.

654 Medical Care Research and Review 76(5)

focuses on AMI patients only (Mellor et al., 2016). Interestingly, for hip or knee
replacement, readmissions outside the 30-day window also decreased. This might be
partly due to the effect of the Bundled Payment for Care Improvement. The cost incen-
tives of bundled payment for lower extremity joint replacement programs took effect
from October 1, 2013, slightly overlapping with the period of this study; and are based
on an episode of care that ends 90 days postdischarge. In terms of discharge status,
there is a consistent trend of AMI patients being decreasingly discharged to SNF.
Although we do not observe any selection against low-income patients, the percentage
of Hispanic patients admitted with heart failure and COPD conditions as well as for
total hip or knee replacement surgeries were on the decline. Taken together, the results
show heterogeneous effects of the HRRP on various outcomes across different
conditions.

We conduct additional analyses to explore the validity of this study’s key results
and report the findings in the Appendix. The triple difference estimates presented in
the Appendix Table A2 yield similar results as the DD estimates reported in Table 2,
column (1), which serves as a sensitivity analysis. In Appendix Table A3, we report
additional validation tests of the robustness of our main DD and triple differences
results. We construct an indicator for 2009, which is prior to the HRRP implementa-
tion, and estimate specifications (1) and (2) using the pre-HRRP years only. None of
the 15 models show any evidence of significant differences in 30-day readmission in
periods prior to the regulations. This partially validates the identifying assumption
that, in the absence of the HRRP, the treatment and comparison groups would have had
similar trends in readmission.

Discussion

We examine patients discharged by Florida hospitals using both DD and triple dif-
ference models to identify the HRRP’s effects. Although our results are comparable
to those in the prior studies when using similar treatment and control groups, the
readmission reduction disappeared or reversed when compared with Medicare
Advantage enrollees and privately insured patients, especially for the three initially
targeted conditions. On the one hand, this may suggest that health care quality has
some commonality across payer groups and hospitals’ efforts in reducing readmis-
sions likely spilled over to other Medicare beneficiaries and privately insured
patients not directly targeted by the HRRP. On the other hand, we must also acknowl-
edge that the larger effect among Medicare Advantage and privately insured patients
may suggest some other contemporaneous downward trend in readmissions for these
particular conditions that is unrelated to the HRRP. Regardless, our results allay
concerns that hospitals may engage in cost-shifting behaviors to offset Medicare
HRRP penalties. Given that hospitals’ incentives to reduce readmissions under the
HRRP only apply to selected Medicare patients, one way that a hospital could recoup
its lost Medicare reimbursements due to excess readmissions would be to readmit
more patients covered by other types of insurance. Our results, however, do not sup-
port such hypothesized cost-shifting behavior.

Chen and Grabowski 655

There is also no evidence of selection on income or admission of Black patients
due to the HRRP. However, the likelihood of Hispanic patients being admitted with
heart failure, COPD, or total hip or knee replacement decreased by 2 to 4 percent-
age points. Studies have found that Black and Hispanic patients experienced higher
readmission rates than Whites for many diagnoses including the target conditions
(heart failure, AMI, pneumonia) covered by the CMS readmissions policies
(Alexander, Grumbach, Remy, Rowell, & Massie, 1999; Jiang, Andrews, Stryer, &
Friedman, 2005; McHugh, Carthon, & Kang, 2010; Rathore et al., 2003). In addi-
tion, many providers believe that minority patients tend to be less educated and less
likely to comply with treatment and thus have higher risks for readmissions (Balsa
& McGuire, 2003; Schulman et al., 1999; van Ryn & Burke, 2000). Because the
current penalty formula does not adjust for hospitals that serve large shares of indi-
gent or minority patients, they may avoid such patients who they believe will reduce
their performance and result in financial penalties (Ryan, 2010). This raises a major
issue of concern as the HRRP may divert resources away from the small percentage
of U.S. hospitals caring for the large majority of elderly Black and Hispanic patients
and exacerbate health care related disparities in access and outcomes (Bhalla &
Kalkut, 2010; McHugh et al., 2010). The fact that there is no evidence of selection
against low-income or Black patients under the HRRP is reassuring, but the
decreased admission of Hispanic patients is concerning, especially given a rela-
tively large Hispanic population in Florida. It is worth noting that the inpatient
discharge data contains limited information on socioeconomic status and a richer
set of sociodemographic variables are needed to keep monitoring disparities across
different minority groups and better understand the underlying factors that may
cause disparities.

In terms of discharge status, our study finds AMI patients were less likely to be
discharged to an SNF. This is consistent with prior studies that report no correlation
or positive relationship between SNF rates and readmission rates for AMI or heart
failure patients (Allen et al., 2011; Chen et al., 2012; Manemann et al., 2017),
which challenges perceptions that SNF care will necessarily reduce readmissions.
A recent study has also found that deliberate reduction in intensive post-acute care
discharges because of incentives created by the Bundled Payment for Care
Improvement model is not associated with increase in readmission rates (Jubelt,
Goldfeld, Chung, Blecker, & Horwitz, 2016). Nonclinical factors such as hospital
ownership of a SNF facility or distance to SNF may affect SNF referral and obscure
the relationship between SNF care and readmission risk. Hospitals with low use of
SNFs, on the other hand, may employ other mechanisms such as home health care
nurses to ensure that patients received sufficient follow-up after discharge and thus
achieve low readmission rates.

This study has limitations. First, we rely on the secondary inpatient administra-
tive data, which are limited in clinical details in different stages of the care delivery
process especially after the patient being discharged. We also do not directly
observe providers’ actual medical decision-making processes at point of care.
Therefore, the specific mechanism that drives the reduced readmissions and lead

656 Medical Care Research and Review 76(5)

to the heterogeneous effects of the HRRP on different target conditions remains a
black box yet to be investigated. Similarly, due to data limitations, we are not able
to examine changes in the observation stays following the HRRP. Finally, although
we use various comparison groups and the triple difference approach as a sensitiv-
ity analysis, our results still rely on the common trend assumption underlying the
DD study design. If hospitals undertook other unobserved quality-improving ini-
tiatives contemporaneous with the HRRP on the target admissions, we would over-
estimate the impact of HRRP.

In conclusion, this study found that the HRRP led to intended readmission reduc-
tions among Florida traditional Medicare beneficiaries. Meanwhile, MA and pri-
vately insured patients with heart attack and heart failure had even lower readmission
rates. There is no evidence of cost shifting, delayed readmission, or selection on
income; however, the HRRP reduced the likelihood of Hispanic patients with target
conditions being admitted by 2 to 4 percentage points. Future research is needed to
understand why readmissions have fallen and how current health care reforms may
affect other outcome dimensions including racial disparity and longer term health
outcomes.

Appendix

Table A1. Difference-in-Differences (DID) Estimates of the Effect of the Hospital
Readmissions Reduction Program on Medicare Advantage and Privately Insured 30-day
Readmissions.

Medicare advantage
patients with GI

conditions as control

Privately insured
patients with GI

conditions as control

DID Impact DID Impact

(1) (2)

(1) Heart attack (acute
myocardial infarction [AMI])

−0.021*** (0.007) −0.015*** (0.005)

(2) Heart failure (HF) −0.009* (0.005) −0.016* (0.008)
(3) Pneumonia (PNE) −0.004 (0.006) −0.004 (0.004)
(4) Chronic obstructive

pulmonary disease (COPD)
0.004 (0.007) 0.005 (0.008)

(5) Total hip or knee angioplasty 0.005 (0.007) −0.023*** (0.006)

Note. GI = gastrointestinal. All models include control variables listed in Table 1 as well as hospital fixed
effects. The standard errors are clustered at hospital level. Robust standard errors in parentheses.
*p < .1. **p < .05. ***p < .01.

657

T
a
b

le
A

2
.

D
iff

er
en

ce
-i
n-

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iff
er
en
ce
-i
n-
D
iff
er
en

ce
s

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ti
m

at
es

o
f
H

o
sp
it
al
R
ea
dm
is
si
o
ns
R
ed
uc
ti
o
n
Pr
o
gr
am
E
ffe
ct
o
n

30
-d

ay
R

ea
dm

is
si

o
ns
.
A
M
I
H
F
PN
C
O
PD
H
ip
/K
ne
e

(1
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)
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)
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)

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st

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01

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0.
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01

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19

0,
63

0
24

9,
70

8
22

2,
74

0
13

3,
91

9
13

5,
56

7
N
ot
e.
A
M
I
=
a
cu
te
m
yo
ca
rd
ia
l i
nf
ar
ct
io
n;
H
F
=
h
ea
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f
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; P
N
=
p
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um
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a;
C
O
PD
=
c
hr
o
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uc
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ul
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ea

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; a

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/K
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e
=
t
o
ta
l h
ip
o
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kn
ee
a
ng
io
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as
ty
/r
ep
la
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en

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c

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pa
ri

so
n

gr
o
up

c
o
ns

is
ts

o
f
M

ed
ic
ar
e
fe
e-
fo
r-
se
rv
ic
e

pa
ti
en

ts
w
it
h

ga
st

ro
in

te
st
in
al
c
o
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it
io
ns
a

s
th

ei
r

pr
im

ar
y

di
ag

no
si

s
du

ri
ng

in
de

x
ad

m
is
si
o
ns

. A
ll

m
o
de
ls
in
cl
ud
e
co
nt
ro
l v
ar
ia
bl
es
li
st
ed
in
T
ab
le
1
a
s
w
el
l a
s
ye
ar
a
nd
h
o
sp
it
al
f
ix
ed
e
ffe
ct
s.
T

he
s

ta
nd

ar
d

er
ro

rs
a

re
c

lu
st

er
ed

a
t

ho
sp

it
al
le
ve

l.
R

o
bu

st
s

ta
nd
ar
d
er
ro

rs
in

p
ar
en
th
es
es
.
*p
< .1 . * *p

< .0

5.
*
**
p
< .0 1.

658 Medical Care Research and Review 76(5)

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship,
and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of
this article.

Notes

1. More details about the Clinical Classifications Software (CCS) for ICD-9-CM can be
found here: https://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp

2. Alternatively, we also identify controls using CCS codes 138-140 and 153-155, and our
results remain robust.

3. https://www.hcup-us.ahrq.gov/toolssoftware/comorbidity/comorbidity.jsp
4. For the five conditions we examined, the national average rate is 19.9 for AMI, 24.5 for

heart failure, 18.2 for pneumonia, 20.7 for COPD, and 5.2 for total hip or knee replacement.
5. The only exception is total hip or knee replacement, of which the DD estimate is insignificant.

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Table A3. Specification Checks: Difference-in-Differences (DD) and Difference-in-
Difference-in-Differences (DDD) Models With Alternate Control Groups.

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conditions as control

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http://healthaffairs.org/blog/2015/10/28/is-observation-status-substituting-for-hospital-readmission

EVIDENCE-
BASED CARE
SHEET

Author
Hillary Mennella, DNP, ANCC-BC

Cinahl Information Systems,

Glendale, CA

Reviewers
Darlene Strayer, RN, MBA

Cinahl Information Systems, Glendale, CA

Jocelyn Cajanap-Gantman, RN, MSN,
FNP, CNS

Sepsis Coordinator, Glendale Adventist
Medical Center

Nursing Executive Practice Council
Glendale Adventist Medical Center,

Glendale, CA

Editor
Diane Pravikoff, RN, PhD, FAAN

Cinahl Information Systems, Glendale, CA

June 22, 2018

Published by Cinahl Information Systems, a division of EBSCO Information Services. Copyright©2018, Cinahl Information Systems. All rights
reserved. No part of this may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, or by
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or information given herein or errors/omissions in the text. It is merely intended as a general informational overview of the subject for the healthcare
professional. Cinahl Information Systems, 1509 Wilson Terrace, Glendale, CA 91206

Hospital Readmissions: United States Centers for
Medicare and Medicaid Services (CMS)

What We Know
› Hospital readmission is an expensive and often avoidable patient care outcome(4,9,12)

› In 2013, an estimated 18% of Medicare patients were readmitted to the hospital within
30 days of discharge. Annual Medicare costs related to readmissions are estimated at
$26 billion, with potentially preventable readmissions accounting for approximately $17
billion of that cost(1)

› All healthcare providers are responsible for identifying patient discharge needs and
developing a thorough discharge plan to reduce the risk for hospital readmissions(3)

› In accordance with the legislative passing of the Affordable Care Act (ACA), the
United States Centers for Medicare and Medicaid Services (CMS) established the
Hospital Readmissions Reduction Program (HRRP) to decrease the frequency of hospital
readmissions of Medicare beneficiaries. Effective October 1, 2012, the provisions of the
HRRP permit the CMS to reduce payments to hospitals under the inpatient prospective
payment system (IPPS) for readmission rates that are reviewed by CMS and determined
to be excessive. The HRRP adjusts hospital reimbursement based on the data for
excessive readmissions following patient admissions for acute myocardial infarction
(AMI), congestive heart failure (CHF), pneumonia (PN), coronary artery bypass graft
(CABG), chronic obstructive lung disease (COPD), stroke, and complications related to
readmissions for total hip/knee replacements (THR/TKR)(4,9,12)

• Under the HRRP, readmission is defined by the CMS as “an admission to a subsection
hospital within 30 days of a discharge from the same or another subsection hospital”(4)

– The hospital readmission rate is calculated from the date of discharge, plus 30 days.
For example, for a patient who is discharged on October 1, the last day for the
postdischarge follow-up period is October 31

– The CMS recognizes 30 days as an industry standard that is strongly influenced by the
quality of care

– Hospital readmissions exclude those involving a patient’s death in the hospital,
enrollment in the Medicare fee-for-service program, hospital admission after at least
30 days post-hospital discharge, and planned hospital readmission (i.e., a nonacute
readmission for a scheduled procedure)

– CMS does not consider preventability when calculating readmission rates(11)

– A standard for identifying and defining what is considered to be a preventable
readmission does not exist(11)

– Hospital readmission rates are assigned a “yes/no” readmission status regardless of the
number of readmissions for a patient during the 30-day postdischarge time period

› Each year during the period 2003–2004, according to billing claims from the CMS, an
estimated 2.3 million Medicare beneficiaries were readmitted to the hospital within 30
days of discharge. Investigators in a study of 11,855,702 Medicare beneficiaries reported
that(8)

• 19.6% were discharged from the hospital and rehospitalized within 30 days
– Of this group, 50.25% did not have a bill for a follow-upvisit to a physician’s office

between the time of discharge to the community and rehospitalization

• 34% were discharged from the hospital and rehospitalized within 30 days
– An estimated 10% were planned hospital

readmissions

• 67.1% of Medicare beneficiaries with medical conditions who were discharged from the hospital were rehospitalized or
died within the first year after discharge

• 51.5% of Medicare beneficiaries who were discharged from the hospital after surgical procedures were rehospitalized or
died within the first year after discharge
– Of this group, 70.5% were rehospitalized with a medical condition

› The CMS began reporting the 30-day mortality rates for the quality outcome measures for AMI and CHF in 2007 and
for PN in 2008. These quality outcome measures are publicly reported in an effort by CMS to increase transparency and
accountability of hospitals for patient care services and treatment(2,12)

• The CMS recommends that hospitals review their 30-day mortality outcome measures in conjunction with their 30-day
hospital readmission data in order to modify the quality and type of care provided to reduce hospital readmissions

› The financial penalties for the HRRP were calculated by the CMS using data from July 2008 through June 2011 for the
readmission rates for all hospitalizations for AMI, PN, and CHF; these rates were adjusted for age, gender, patient frailty,
and coexisting medical conditions and compared with the actual readmission rates over the same period of time using a
methodology that is endorsed by the National Quality Forum (NQF)(4,9,12)

• A hospital’s calculated readmission rate for MI, PN, CHF, COPD, CABG, stroke, and THR/TKR is the performance
measure of that hospital’s readmission rate compared with the national average for a hospital’s set of patients with the same
applicable conditions

• For the fiscal year 2013, hospital readmission rates were calculated from data on discharges from July 1, 2008, through
June 30, 2011

• In the fiscal year 2014, an estimated 80% of hospitals were penalized, at a cost of $428 million(1)

• Kaiser Health News (KHN) reported 4 out of 5 hospitals were penalized for readmissions based on patient discharge data
analyzed between July 2013 and June 2016(13)

– The average penalty between October 1, 2017 and September 30, 2018 is expected to be 0.73% for each payment
Medicare makes per patient(13)

› The CMS levied financial penalties of up to 1% of hospital reimbursement rates for readmission of Medicare beneficiaries.
The financial penalties increased to 2% in 2014 and to a maximum of 3% in 2015(9)

› The CMS 30-day hospital readmission measures are federally mandated to be publicly available under the Hospital Inpatient
Quality Reporting Program(12)

› The CMS provides hospitals with Hospital-Specific Reports (HSRs) under the Hospital Inpatient Quality Reporting
(IQR) program to promote hospital quality improvement efforts. The HSRs provide detailed information on a hospital’s
readmission rates, discharge data, and specific risk factor data(4,12)

› Investigators analyzing the publicly available data from July 2008 through June 2011 for 3,282 hospitals found that
large hospitals, teaching hospitals, and safety-net hospitals (i.e., a hospital system that provides care to a large number of
uninsured or low-income patients) had higher readmission rates compared with small hospitals and non-teaching hospitals.
Of this sample, 2,189 hospitals, or 66.7% of hospitals, will receive financial penalties as a result of the HRRP. Investigators
call for additional research to determine why large hospitals, teaching hospitals, and safety-net hospitals have higher
readmission rates than small and non-teaching hospitals(10)

› Researchers evaluating the impact of community factors on hospital readmission rates noted that a large portion of
readmission rates is affected by the characteristics of the local healthcare community (e.g., quality of nursing homes, access
to primary care), specifically the county where the hospital is located. This suggests that penalizing hospitals with high
readmission rates might not be equitable and that interventions aimed at community-based readmission reduction strategies
might result in improved outcomes(6)

› As new data emerge on hospital readmission rates, the CMS should consider the impact on underserved medical
communities and make necessary adjustments to the policies regarding hospital readmission. Debate exists about financially
penalizing hospitals for excessive readmission rates. Experts argue that the CMS rules are inherently discriminatory toward
hospitals that serve low-income groups and/or severely ill patients. Experts argue the following issues:(9,12)

• At the inception of the HRRP, the CMS did not adjust for socioeconomic status (SES) or severity of comorbid illness in the
calculation of the hospital readmission measures

– The CMS argued that adjustment for SES implies that it is acceptable for low-income patient groups to receive less than
standard quality of care

– Experts contend that the CMS should adjust for SES to place all hospitals at the same level
– Researchers have suggested weighting HRRP penalties according to the timing of readmissions. For example, hospital

readmission within the first few days after discharge can indicate poor discharge planning compared with hospital
readmission 3 weeks after discharge, which is more likely to indicate the severity of the patient’s underlying illness and/
or comorbid diseases. This suggestion offers hospitals the opportunity to make improvements to their discharge planning
process while caring for severely ill and low-income groups of patients

• Events leading to hospital readmissions might be out of the hospital’s control. Hospitals serving a larger population of
patients from a lower SES often have higher rates for readmission compared to the national average resulting in lower
Medicare reimbursements. Patients from a lower SES can have difficulty procuring follow-up appointments, food, and
medications after discharge(5)

– Patients that are eligible for Medicare and Medicaid are defined as “dual-eligibles.” They tend to be medically complex
patients with high levels of healthcare utilization. As a result of the 21st Century Cures Act of 2016 the CMS proposed
changes for calculating financial penalties under the HRRP beginning fiscal year 2019 among hospitals with high
readmission rates of patients from low SES backgrounds. The new calculations are risk-adjustment strategies that include
comparisons of social economic risk factors among hospitals(7,14)

• The HRRP was criticized by experts that the program had the potential to exacerbate disparities in patient care and generate
disincentives to provide care for patients with severe illness and/or complex comorbidities

What We Can Do
› Become knowledgeable about hospital readmissions so you can adhere to the CMS quality outcome measures and the

HRRP; share this information with your colleagues
› Review publicly available hospital readmission rates to compare your organization against national benchmarks; for more

information, see http://www.qualitynet.org
› Collaborate with colleagues in your facility to

• review your HSR to promote hospital quality improvement efforts
• develop an individualized discharge plan for your patients
• provide high-quality healthcare to your patients to promote positive patient outcomes and reduce the risk for hospital

readmissions

Coding Matrix
References are rated using the following codes, listed in order of strength:

M Published meta-analysis

SR Published systematic or integrative literature review

RCT Published research (randomized controlled trial)

R Published research (not randomized controlled trial)

C Case histories, case studies

G Published guidelines

RV Published review of the literature

RU Published research utilization report

QI Published quality improvement report

L Legislation

PGR Published government report

PFR Published funded report

PP Policies, procedures, protocols

X Practice exemplars, stories, opinions

GI General or background information/texts/reports

U Unpublished research, reviews, poster presentations or
other such materials

CP Conference proceedings, abstracts, presentation

References
1. Boozary, A. S., Manchin, J., III, & Wicker, R. F. (2015). The Medicare Hospital Readmissions Reduction Program: Time for reform. JAMA: Journal of the American Medical

Association, 314(4), 347-348. doi:10.1001/jama.2015.6507 (R)

2. Centers for Medicare and Medicaid Services. (2017). Outcomes measures. Retrieved June 15, 2018, from
https://www.cms.gov/medicare/quality-initiatives-patient-assessment-instruments/hospitalqualityinits/outcomemeasures.html (G)

3. Centers for Medicare and Medicaid Services. (2013). Revision to State Operations Manual (SOM), Hospital Appendix A – Interpretive Guidelines for 42 CFR 482.43, Discharge
Planning. Retrieved June 15, 2018, from
https://www.cms.gov/Medicare/Provider-Enrollment-and-Certification/SurveyCertificationGenInfo/Policy-and-Memos-to-States-and-Regions-Items/Survey-and-Cert-Letter-13-32.html
(G)

4. Centers for Medicare and Medicaid Services. (2018, April 27). Readmissions reduction program. Retrieved June 15, 2018, from
http://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/Readmissions-Reduction-Program.html (G)

5. Changes to readmissions rule will help, but no panacea. (2017). Case Management Advisor, 28(9), 14-15. (X)

6. Herrin, J., St. Andre, J., Kenward, K., Joshi, M. S., Audet, A. J., & Hines, S. C. (2015). Community factors and hospital readmission rates. Health Services Research, 50(1),
20-39. doi:10.111/1475-6773.12177 (R)

7. Hospitals can now factor socioeconomic status into readmissions. (2017). Hospital Case Management, 25(3), 41-42. (GI)

8. Jencks, S. F., Williams, M. V., & Coleman, E. A. (2009). Rehospitalizations among patients in the Medicare fee-for-service program. New England Journal of Medicine, 360(14),
1418-1428. doi:10.1056/NEJMsa0803563 (R)

9. Joynt, K. E., & Jha, A. K. (2013). A path forward on Medicare readmissions. New England Journal of Medicine, 368(13), 1175-1177. doi:10.1056/NEJMp1300122 (GI)

10. Joynt, K. E., & Jha, A. K. (2013). Characteristics of hospitals receiving penalties under the Hospital Readmissions Reduction Program. JAMA, 309(4), 342-343. doi:10.1001/
jama.2012.94856 (R)

11. Lavenberg, J. G., Leas, B., Unscheid, C. A., Williams, K., Goldman, D. R., & Kripalani, S. (2014). Assessing preventability in the quest to reduce hospital readmissions. Journal
of Hospital Medicine, 9(9), 598-603. doi:10.1002/jhm.2226 (R)

12. QualityNet. (n.d.). Readmission measures overview. Retrieved June 15, 2018, from http://www.qualitynet.org/dcs/ContentServer?c=Page&pagename=QnetPublic%2FPage
%2FQnetTier2&cid=1219069855273 (G)

13. Rau, J. (2017). Medicare readmission penalties for hospitals continue under Trump. Retrieved June 15, 2018, from
http://www.healthcarefinancenews.com/news/medicare-readmission-penalties-hospitals-continue-under-trump (GI)

14. Whitman, E. (2017). Dual-eligibles could offer relief for hospital readmissions penalties. Modern Healthcare, 47(17), 0010. (GI)

Association of the Hospital Readmissions Reduction Program
With Mortality Among Medicare Beneficiaries Hospitalized
for Heart Failure, Acute Myocardial Infarction, and Pneumon

ia

Rishi K. Wadhera, MD, MPP, MPhil; Karen E. Joynt Maddox, MD, MPH; Jason H. Wasfy, MD, MPhil; Sebastien Haneuse, PhD;
Changyu Shen, PhD; Robert W. Yeh, MD, MSc

IMPORTANCE The Hospital Readmissions Reduction Program (HRRP) has been associated with
a reduction in readmission rates for heart failure (HF), acute myocardial infarction (AMI), and
pneumonia. It is unclear whether the HRRP has been associated with change in patient mortality.

OBJECTIVE To determine whether the HRRP was associated with a change in patient mortality.

DESIGN, SETTING, AND PARTICIPANTS Retrospective cohort study of hospitalizations for HF,
AMI, and pneumonia among Medicare fee-for-service beneficiaries aged at least 65 years
across 4 periods from April 1, 2005, to March 31, 2015. Period 1 and period 2 occurred before
the HRRP to establish baseline trends (April 2005-September 2007 and October
2007-March 2010). Period 3 and period 4 were after HRRP announcement (April 2010 to
September 2012) and HRRP implementation (October 2012 to March 2015).

EXPOSURES Announcement and implementation of the HRRP.

MAIN OUTCOMES AND MEASURES Inverse probability–weighted mortality within 30 days of
discharge following hospitalization for HF, AMI, and pneumonia, and stratified by whether
there was an associated readmission. An additional end point was mortality within 45 days of
initial hospital admission for target conditions.

RESULTS The study cohort included 8.3 million hospitalizations for HF, AMI, and pneumonia,
among which 7.9 million (mean age, 79.6 [8.7] years; 53.4% women) were alive at discharge.
There were 3.2 million hospitalizations for HF, 1.8 million for AMI, and 3.0 million for pneumonia.
There were 270 517 deaths within 30 days of discharge for HF, 128 088 for AMI, and 246 154 for
pneumonia. Among patients with HF, 30-day postdischarge mortality increased before the
announcement of the HRRP (0.27% increase from period 1 to period 2). Compared with this
baseline trend, HRRP announcement (0.49% increase from period 2 to period 3; difference in
change, 0.22%, P = .01) and implementation (0.52% increase from period 3 to period 4;
difference in change, 0.25%, P = .001) were significantly associated with an increase in
postdischarge mortality. Among patients with AMI, HRRP announcement was associated with a
decline in postdischarge mortality (0.18% pre-HRRP increase vs 0.08% post-HRRP
announcement decrease; difference in change, −0.26%; P = .01) and did not significantly change
after HRRP implementation. Among patients with pneumonia, postdischarge mortality was
stable before HRRP (0.04% increase from period 1 to period 2), but significantly increased after
HRRP announcement (0.26% post-HRRP announcement increase; difference in change, 0.22%,
P = .01) and implementation (0.44% post-HPPR implementation increase; difference in change,
0.40%, P < .001). The overall increase in mortality among patients with HF and pneumonia was mainly related to outcomes among patients who were not readmitted but died within 30 days of discharge. For all 3 conditions, HRRP implementation was not significantly associated with an increase in mortality within 45 days of admission, relative to pre-HRRP trends.

CONCLUSIONS AND RELEVANCE Among Medicare beneficiaries, the HRRP was significantl

y

associated with an increase in 30-day postdischarge mortality after hospitalization for HF and
pneumonia, but not for AMI. Given the study design and the lack of significant association of
the HRRP with mortality within 45 days of admission, further research is needed to
understand whether the increase in 30-day postdischarge mortality is a result of the policy.

JAMA. 2018;320(24):2542-2552. doi:10.1001/jama.2018.1923

2

Editorial page 2539

Supplemental content

Author Affiliations: Author
affiliations are listed at the end of this
article.

Corresponding Authors: Robert W.
Yeh, MD, MSc, and Changyu Shen,
PhD, Smith Center for Outcom

es

Research in Cardiology, Beth Israel
Deaconess Medical Center, 375
Longwood Ave, Boston, MA 02215
(ryeh@bidmc.harvard.edu).

Research

JAMA | Original Investigation

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T he Hospital Readmissions Reduction Program (HRRP)was established under the Affordable Care Act (ACA) in2010 and required that the Centers for Medicare & Med-
icaid Services (CMS) impose financial penalties on hospitals
with higher-than-expected 30-day readmission rates for pa-
tients with heart failure, acute myocardial infarction, and pneu-
monia, beginning in 2012.1 After the announcement of the
HRRP, readmission rates among Medicare beneficiaries de-
clined for target conditions nationwide.2,3 Recently, how-
ever, policy makers and physicians have raised concern that
the HRRP may have also had unintended consequences
that adversely affected patient care, potentially leading to in-
creased mortality.4,5 For instance, the financial penalties im-
posed by the HRRP may have inadvertently pushed some phy-
sicians to avoid indicated readmissions, potentially diverted
hospital resources and efforts away from other quality im-
provement initiatives, or worsened quality of care at resource-
poor hospitals that are often penalized by the program. How-
ever, it is also possible that the same mechanisms by which
some hospitals have reduced readmissions, such as im-
proved coordination and transitions of care, resulted in reduc-
tions in mortality.

Understanding whether the HRRP has been associated
with changes in mortality at the patient level is important as
policy makers evaluate this program, particularly given the
ongoing expansion of the HRRP to include other conditions

6

and the almost $2 billion in financial penalties that have been
imposed on hospitals since 2012.7 This study aims to answer
3 questions. First, compared with past trends, was the
announcement or implementation of the HRRP associated
with a change in mortality within 30 days of discharge fol-
lowing hospitalization for heart failure, acute myocardial
infarction, or pneumonia? Second, was the HRRP associated
with a change in the distribution of patients who experienced
death and no readmission, readmission and no death, read-
mission and death, or no death and no readmission during
the 30 days after discharge? Third, was the HRRP associated
with a change in mortality within 45 days of hospital admis-
sion for target conditions?

Methods
Institutional review board approval, including waiver of the
requirement of participant informed consent because the data
were deidentified, was provided by the Beth Israel Deacon-
ess Medical Center.

Study Cohort
We used Medicare Provider Analysis and Review files to iden-
tify hospital admissions and discharges at short-term acute care
hospitals from April 1, 2005, through March 31, 2015, with
a principal discharge diagnosis of heart failure, acute myo-
cardial infarction, or pneumonia. Study cohorts were de-
fined using International Classification of Diseases, Ninth
Revision, Clinical Modification codes used in the publicly re-
ported CMS readmission and mortality measures.8-10 We in-
cluded Medicare beneficiaries aged 65 years or older in the

analysis. We excluded patients who were discharged against
medic al advice, were not enrolled in Medic are fee-for-
service for at least 30 days after discharge (absent death),
or were enrolled in Medicare fee-for-service for less than

1

year before hospitalization. Transfers to other hospitals were
linked to a single index hospitalization. To examine 30-day
postdischarge outcomes, we also excluded patients who
died during hospitalization. Comorbidities were defined using
CMS hierarchical condition categories based on Medicare
claims up to 1 year before hospitalization.11 Specifically, we used
covariates in the CMS risk-adjustment models for heart fail-
ure, acute myocardial infarction, and pneumonia,12-14 as has
been done in previous studies.2,15 The race/ethnicity of all pa-
tients was identified based on claims files and was desig-
nated into the following fixed categories: white, black, or other.
Race/ethnicity was included as a covariate in the analysis be-
cause it is associated with mortality for target conditions.

16

Study Periods

We identified 4 nonoverlapping study periods of equal dura-
tion for index hospitalization. We chose to evaluate differ-
ences in outcomes between time periods, rather than annual
trends, for 2 reasons. First, we were interested in changes in
outcomes among time periods defined by their relationship to
the announcement and implementation of the HRRP, rather
than within-period trends. Second, this strategy avoids as-
sumptions on how the HRRP imposes its effect on different pa-
tient groups (eg, assumptions on main effects and interaction
terms) and of a linear relationship between outcomes and time
and continuous confounders in a conventional logistic or mul-
tinominal regression model.

We identified 2 study periods before the HRRP was estab-
lished to examine baseline trends in outcomes. The first study
period included hospitalizations from April 2005 to Septem-
ber 2007 (period 1) and the second included hospitalizations
from October 2007 to March 2010 (period 2). Two periods af-
ter the HRRP was established were also included: 1 following
the initial announcement of HRRP with passage of the ACA
from April 2010 through September 2012 (period 3) and the
other between October 2012 and March 2015 (period 4), which

Key Points
Question Was the announcement and implementation of the
Hospital Readmissions Reduction Program (HRRP) associated with
an increase in patient-level mortality?

Findings In this retrospective cohort study that included
approximately 8 million Medicare beneficiary fee-for-service
hospitalizations from 2005 to 2015, implementation of the HRRP
was associated with a significant increase in trends in 30-day
postdischarge mortality among beneficiaries hospitalized for heart
failure and pneumonia, but not for acute myocardial infarction.

Meaning There was a statistically significant association with
implementation of the HRRP and increased post-discharge
mortality for patients hospitalized for heart failure and
pneumonia, but whether this finding is a result of the policy
requires further research.

Association of the Hospital Readmissions Reduction Program With Heart Failure, AMI, and Pneumonia Mortality Original Investigation Research

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is when the HRRP was implemented and hospitals were sub-
jected to financial penalties. For patients with multiple hos-
pitalizations within a time period, 1 index hospitalization was
randomly selected for each condition.

Outcomes
Patient mortality within 30 days of discharge after a hospital-
ization (postdischarge mortality) for heart failure, acute myo-
cardial infarction, and pneumonia was evaluated, which has
been done in previous hospital-level analyses.17-19 The follow-
ing 30-day postdischarge outcome subgroups were also ex-
amined: (1) death and no readmission, (2) readmission and
death, (3) readmission and no death, and (4) no readmission
and no death. These subgroup outcomes were examined to try
to provide mechanistic insights on the relationship between
readmission and mortality. To fully assess trends in mortality
related to a complete clinical episode, 45-day patient mortal-
ity rates following admission (postadmission mortality) were
also evaluated, because efforts to reduce readmissions could
potentially encompass care during hospitalization and might
influence discharge timing and location of death. This mea-
sure included varying hospital lengths of stay and captured
both in-hospital and 30-day postdischarge deaths for the ma-
jority of the cohort.

Statistical Analysis
To account for a potential imbalance in case mix between study
periods, a propensity score approach (ie, the probability of
being in a specific period given the demographics and comor-
bidities of the patient and calendar month of hospitalization)
was used to standardize populations among periods. Patient
demographics, comorbidities, and seasonal indicators (calen-
dar month) from period 4 were used as a reference to re-
weight observed outcomes in all other study periods. Logis-
tic regression models were fit on data from periods 1 and 4 to
obtain a propensity score for period 1. The propensity score was
then used to weight the outcomes in period 1, generating event
rates through inverse probability weighting (IPW) that would

have been observed if period 1 had the same case mix as pe-
riod 4. Similarly, separate logistic regression models were fit
to data from periods 2 and 4 and periods 3 and 4 to provide
IPW-adjusted event rates in periods 2 and 3, respectively. This
approach allowed the calculated distribution of each out-
come in each of the 4 periods to be based on the same case mix
(ie, the case mix from period 4).20 Because the primary aim
was to understand the association of the HRRP with mortal-
ity at the individual level, we did not examine hospital-level
effects in the analysis.

To establish the change in rates of outcomes after the an-
nouncement of the HRRP, the change in event rates between
periods 2 and 3 was calculated. Similarly, the change in rates
of outcomes between periods 3 and 4 was also calculated to
examine the change in outcomes between the announce-
ment and the implementation of the HRRP (Figure 1).

To isolate the association between the HRRP and the out-
comes, we sought to remove secular trends for each out-
come. To do so, the change in outcomes between periods 1 and
2 was computed to establish a baseline trend in outcomes be-
fore the announcement and implementation of the HRRP. This
difference was then subtracted from the change in outcomes
after the announcement of the HRRP (between periods 2 and
3) to account for trends that were unrelated to the HRRP. Simi-
larly, the baseline difference was also subtracted from the
change in outcomes after the implementation of the HRRP, be-
tween periods 3 and 4.

Additional Analyses
Several sensitivity analyses were performed. First, patients
enrolled in hospice were excluded because greater use of
hospice care at the end of life might shift deaths that previ-
ously occurred within a hospital to the postdischarge setting
over time.21,22 Second, because 1 hospitalization was ran-
domly selected for patients that experienced multiple hospi-
talizations in a given study period, the main analysis was
repeated using the first hospitalization for each patient in
each study period as well as all hospitalizations for each

Figure 1. Study Periods and Analytic Approach in a Study of the Association Between the Hospital
Readmissions Reduction Program (HRRP) and Mortality

Period 1
(April 2005-

September 2007)

Period 2
(October 2007-

March 2010)

Period 3
(April 2010-

September 2012)

Period

4

(October 2012-

April 2015)

Baseline change
in mortality before
HRRP announcement

Difference in change in mortality prior
to HRRP (A) compared with change
after HRRP announcement (B)

Difference in change in mortality before HRRP (A)
compared with change after HRRP implementation (C)

Change in mortality
after HRRP
announcement

Change in mortality
after HRRP

implementation

HRRP Announcement
(April 2010)

HRRP Implementation
(October 2012)

Calculation A

Calculation

Calculation

Calculation B Calculation C

Research Original Investigation Association of the Hospital Readmissions Reduction Program With Heart Failure, AMI, and Pneumonia Mortality

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patient. Third, the entire analysis for postdischarge mortality
was repeated using outcome regression within each study
period to generate predicted outcomes for the case-mix in
period 4, which were then directly compared across periods
to ensure the results were not sensitive to the analytic
approach used.

More details on the methodologic approach are provided
in the Supplement. Significance testing was performed using
z tests, with standard error estimates that accounted for in-
verse probability weighting. Statistical tests were 2-sided at a
significance level of .05. The false discovery rate (FDR) based
multiple comparison procedure was used to assess the statis-
tical significance of the difference in the change in mortality-
related end points (eg, aggregate mortality, mortality with or
without readmission) at the FDR level of 0.05.23,24 Analyses
were performed using SAS version 9.4 (SAS Institute).

Results
There were 8 326 688 Medicare fee-for-service hospitaliza-
tions for heart failure, acute myocardial infarction, and pneu-
monia from April 1, 2005, to March 31, 2015, among which
7 948 937 patients were alive at hospital discharge. The mean
(SD) age of the study population was 79.6 (8.7) years,
4 246 45 4 partic ipants (53.4%) were women, 6 802 296
(85.6%) were white, and 738 198 (9.3%) were black. There
were 3.2 million hospitalizations for heart failure, 1.8 million
for acute myocardial infarction, and 3.0 million for pneumo-
nia and, overall, there were 270 517 deaths from heart failure,
128 088 deaths from ac ute myoc ardial infarction, and
246 154 deaths from pneumonia within 30 days of discharge.
Baseline patient demographics were similar among the 4
study periods; comorbidities are shown in Table 1 for patients
alive at discharge. Observed trends in 30-day postdischarge
and 45-day postadmission outcomes for target conditions are
shown in Figure 2 and eTables 1 and 2 in the Supplement.

HRRP and 30-Day Postdischarge Mortality
Among patients with heart failure, IPW-adjusted postdis-
charge mortality (Figure 3A and eTable 3 in the Supplement)
increased before the announcement or implementation
of the HRRP (0.27% increase from period 1 to period 2;
Table 2). Relative to this baseline trend, the announcement
of the HRRP was significantly associated with an increase in
postdischarge mortality (0.49% increase from period 2 to
period 3; 0.22% difference between the change from period
1 to period 2 and period 2 to period 3; P = .01). An analysis
stratified by whether there was an associated readmission
showed that this change was entirely driven by a significant
increase in mortality without readmission (0.27% increase
from period 1 to period 2 vs 0.53% increase from period 2 to
period 3; 0.26% difference between the change from period
1 to period 2 and period 2 to period 3; P < .001). In addition, HRRP implementation was significantly associated with an increase in postdischarge mortality overall relative to base- line trends (0.52% increase from period 3 to period 4; 0.25% difference between the change from period 1 to period 2 and

period 3 to period 4; P = .001), which was also explained by
an increase in death without readmission.

In contrast, among patients with acute myocardial infarc-
tion (Figure 3B), HRRP announcement was significantly asso-
ciated with a decline in postdischarge mortality (Table 2;
0.18% increase from period 1 to period 2 vs 0.08% decrease
from period 2 to period 3; −0.26% difference between the
change from period 1 to period 2 and period 2 to period 3;
P = .01). Compared with baseline trends, HRRP implementa-
tion was not associated with a significant change in mortality
(0.15% increase from period 3 to period 4; −0.03% difference
between the change from period 1 to period 2 and period 3 to
period 4; P = .69).

Postdischarge mortality among patients with pneumonia
(Figure 3C) was relatively stable before the HRRP (0.04%
increase from period 1 to period 2), but increased signifi-
cantly after announcement of the HRRP (Table 2; 0.26%
increase from period 2 to period 3; 0.22% difference between
the change from period 1 to period 2 and period 2 to period 3;
P = .01). This overall change was driven by an increase in
patients who were not readmitted but died within 30 days of
discharge (0.09% increase from period 1 to period 2 vs 0.32%
increase from period 2 to period 3; 0.23% difference between
the change from period 1 to period 2 and period 2 to period 3;
P = .003). In addition, compared with baseline trends, HRRP
implementation was also significantly associated with an
increase in mortality overall (0.44% increase from period 3 to
period 4; 0.40% difference between the change from period 1
to period 2 and period 3 to period 4; P < .001) and among stratified mortality outcomes of death and no readmission (0.09% from period 1 to period 2 vs 0.38% from period 3 to period 4; 0.30% difference between the change from period 1 to period 2 and period 3 to period 4; P < .001) and readmis- sion and death (0.05% decrease from period 1 to period 2 vs 0.05% increase from period 3 to period 4; 0.11% difference between the change from period 1 to period 2 and period 3 to period 4; P = .003).

All P values less than .05 for the 18 comparisons involv-
ing 3 end points (total mortality, mortality without readmis-
sion, and mortality with readmission), 2 differences in change
(post-HRRP announcement trends and post-HRRP implemen-
tation trends compared with pre-HRRP trends) and 3 condi-
tions (heart failure, acute myocardial infarction, and pneu-
monia) were also significant at the FDR level of 0.05 (Table 2).

Other 30-Day Postdischarge Outcomes
Inverse probability-weighted readmissions without death
within 30 days declined significantly following the announce-
ment and implementation of the HRRP compared with the
years preceding the HRRP for all 3 target conditions (Table 2).
Trends across study periods in rates of patients who were not
readmitted and were alive within 30 days of discharge are also
shown in Table 2 and eTable 3 in the Supplement.

HRRP and 45-Day Postadmission Mortality
Trends in IPW-adjusted postadmission mortality rates are
shown in Figure 4 and eTable 4 in the Supplement. Among pa-
tients hospitalized for heart failure, postadmission mortality

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rates steadily increased before the announcement of the HRRP
(Table 2; 0.15% increase from period 1 to period 2). Compared
with this baseline trend, the HRRP announcement was sig-
nificantly associated with an increase in mortality (0.42% in-
crease from period 2 to period 3; 0.27% difference between the
change from period 1 to period 2 and period 2 to period 3;
P = .01). However, mortality did not significantly change af-
ter HRRP implementation (0.32% increase from period 3 to pe-
riod 4; 0.17% difference between the change from period 1 to
period 2 and period 3 to period 4; P = .06).

Postadmission mortality declined among patients hospi-
talized for acute myocardial infarction before the announce-
ment of the HRRP (0.24% decline from period 1 to period 2), a
trend that did not significantly change after the HRRP an-
nouncement (0.35% decline from period 2 to period 3; −0.12%

difference between the change from period 1 to period 2 and
period 2 to period 3; P = .39). Following the HRRP implemen-
tation, postadmission mortality continued to decline (0.44%
from period 3 to period 4), but did not significantly differ from
baseline trends (−0.21% difference between the change from
period 1 to period 2 and period 3 to period 4: P = .06).

Among patients hospitalized for pneumonia, postadmis-
sion mortality was relatively stable before the HRRP (0.05%
increase from period 1 to period 2), and did not significantly
change after the HRRP announcement (0.15% decline from pe-
riod 2 to period 3; −0.20% difference between the change from
period 1 to period 2 and period 2 to period 3; P = .07) and imple-
mentation (0.14% increase from period 3 to period 4; 0.09%
difference between the change from period 1 to period 2 and
period 3 to period 4; P = .30).

Table 1. Baseline Characteristics of Patients Discharged After Hospitalization for Heart Failure,
Acute Myocardial Infarction, or Pneumoniaa

Participants, %
Period 1
(April 2005-
September 2007)

Period 2
(October 2007-
March 2010)

Period 3
(April 2010-
September 2012)

Period 4
(October 2012-
March 2015)

Hospitalizations 2 283 774 2 011 915 1 857 337 1 795 911

Demographics

Age, mean (SD), y 79.5 (8.5) 79.7 (8.7) 79.7 (8.9) 79.6 (9.0)

Women 54.4 53.7 53.1 52.2

Men 45.6 46.3 46.9 47.

8

Race/ethnicity

White 85.9 85.8 85.4 85.1

Black 9.2 9.2 9.4 9.4

Otherb 4.9 5.0 5.2 5.5

Cardiovascular comorbidities

Chronic atherosclerosis 53.0 52.6 52.4 50.

0

Diabetes 33.9 34.1 35.3 36.0

Hypertension 60.6 66.3 69.1 67.6

History of acute myocardial infarction 5.1 5.2 5.2 5.1

History of heart failure 27.2 26.4 26.8 26.1

Peripheral vascular disease 8.6 8.7 8.4 7.7

Unstable angina 3.4 2.9 2.7 2.6

Valvular heart disease 22.7 17.6 17.2 17.0

Other comorbidities

Anemia 28.5 30.2 32.3 32.0

COPD 39.6 34.8 34.5 33.8

Cancer 9.5 9.8 9.9 9.7

Cerebrovascular disease 5.1 5.0 4.9 4.5

Dementia 13.7 14.2 12.9 6.8

Depression 8.4 8.1 8.5 8.2

Functional disability 2.9 3.2 3.4 3.3

Liver disease 1.0 1.0 1.1 1.3

Malnutrition 4.6 6.5 7.7 8.2

Psychiatric disorder 2.8 3.2 3.3 3.2

Kidney failure 14.0 18.2 21.2 21.9

Respiratory failure 6.6 8.7 10.2 11.5

Substance abuse 6.9 6.6 7.0 7.3

Trauma 7.5 7.4 7.2 6.6

Length of stay, mean (SD), d 5.6 (4.9) 5.5 (4.8) 5.2 (4.5) 5.1 (4.4)

Abbreviation: COPD, chronic
obstructive pulmonary disease.
a Data are reported as percentages

unless otherwise noted. HRRP
announcement was in April 20

10

and implementation was
in October 2012.

b Race/ethnicity denoted as Asian,
Hispanic, North American Native,
other, or unknown.

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Additional Analyses
As a sensitivity analysis, we excluded patients receiving hos-
pice care and observed patterns in postdischarge mortality that
paralleled our primary analysis (eTable 5 in the Supplement).
After excluding patients receiving hospice care, postdis-
charge mortality among patients hospitalized for heart fail-
ure and pneumonia were declining before the announce-
ment and implementation of the HRRP, but significantly
increased after the announcement and implementation due
to an increase in mortality without readmission (eTable 6 in
the Supplement). Trends in hospice deaths within 30 days of
discharge by condition are shown in eTables 7 and 8 in the
Supplement. Trends in postdischarge mortality also re-
mained similar when the analysis was restricted to the first hos-
pitalization for each patient in each period (eTables 9 and 10
in the Supplement) or included all hospitalizations for each pa-

tient (eTables 11 and 12 in the Supplement). In addition, find-
ings were consistent using the outcome regression-based ap-
proach (eTables 13 and 14 in the Supplement).

Discussion
Overall, the announcement and implementation of the HRRP
was associated with a significant increase in mortality within
30 days of discharge among Medicare beneficiaries hospi-
talized for heart failure and pneumonia, but not for acute
myocardial infarction. Although 30-day postdischarge mor-
tality for heart failure was increasing before the HRRP, this
increase accelerated after the announcement and implemen-
tation of the program. In addition, postdischarge mortality
for pneumonia was stable before the HRRP, but increased

Figure 2. Observed 30-Day Postdischarge Mortality for Target Conditions Before and After the Announcement and Implementation
of the Hospital Readmissions Reduction Program (HRRP)

10
8
6
4
2
0

30
-D

ay
P

os
td

is
ch

ar
ge

M
or

ta
lit

y,
%

Study Periods

No. of
hospitalizations

Heart failureA
HRRP

announcement
HRRP

implementation

Period 1
(2005-2007)

911 244

Period 2
(2007-2010)

805 918

Period 3
(2010-2012)

734 675

Period 4
(2012-2015)

720 228

10
8
6
4
2
0
30
-D
ay
P
os
td
is
ch
ar
ge

M
or

ta
lit
y,
%
Study Periods
No. of
hospitalizations

PneumoniaC
HRRP

announcement
HRRP
implementation
Period 1
(2005-2007)

891 966

Period 2
(2007-2010)

763 378

Period 3
(2010-2012)

704 233

Period 4
(2012-2015)

659 274

10
8
6
4
2
0
30
-D
ay
P
os
td
is
ch
ar
ge
M
or
ta
lit
y,
%
Study Periods
No. of
hospitalizations

Acute myocardial infarctionB
HRRP

announcement
HRRP
implementation
Period 1
(2005-2007)

480 564

Period 2
(2007-2010)

442 619

Period 3
(2010-2012)

418 429

Period 4
(2012-2015)

419 409

Readmission and death

Death and no readmission

Aggregate death

Readmission and death
Death and no readmission
Aggregate death
Readmission and death
Death and no readmission
Aggregate death

Trends in observed overall 30-day postdischarge mortality and 30-day
postdischarge mortality stratified by whether there was an associated
readmission for (A) heart failure (B) acute myocardial infarction,

and (C) pneumonia. Given the large sample size, CIs for all point estimates are
very narrow and therefore not depicted.

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after announcement and implementation of the program.
The increase in mortality for heart failure and pneumonia
were driven mainly by patients who were not readmitted
within 30 days of discharge.

Postdischarge mortality was first evaluated because this
is the period when many potential changes in care incentiv-
ized by the HRRP, intended to lower readmissions, could
manifest in terms of mortality.17 In addition, mortality within
45 days of initial admission was also evaluated, because
efforts to reduce readmissions could potentially encompass
care during the index hospitalization and might influence
discharge timing and location of death. Although announce-
ment of the HRRP was associated with a significant increase
in mortality for patients with heart failure using this alternate
end point, no association was observed between HRRP
implementation and increased mortality for all conditions.
The difference between findings for postdischarge and post-
admission mortality could potentially be explained by
in-hospital deaths, which were steadily declining for target

conditions in the decade before the announcement and
implementation of the HRRP.25,26 The postadmission mortal-
ity measure included both in-hospital and postdischarge
deaths; thus secular declines in in-hospital deaths may have
counterbalanced the increase in postdischarge mortality
observed after the announcement and implementation of the
HRRP. Hospitals may have also changed practices so that
high-risk patients, over time, were discharged earlier, leading
to a shift of some deaths from the inpatient to the outpatient
setting that was unrelated to the HRRP. Such shifts, however,
would need to have accelerated at the time of the announce-
ment and implementation of the HRRP to explain the con-
comitant increase in postdischarge mortality.

Most concerning, however, is the possibility that the
relationship between the HRRP and postdischarge mortality
for heart failure and pneumonia is causal, indicating that
the HRRP led to changes in quality of care that adversely af-
fected patients. Financial incentives aimed at reducing
readmissions were up to 10- to 15-fold greater under the HRRP

Figure 3. Inverse Probability-Weighted 30-Day Postdischarge Mortality for Target Conditions Before and After the Announcement
and Implementation of the Hospital Readmissions Reduction Program (HRRP)

10
8
6
4
2
0
30
-D
ay
P
os
td
is
ch
ar
ge
M
or
ta
lit
y,
%
Study Periods
Heart failureA
HRRP
announcement
HRRP
implementation
Period 1
(2005-2007)
Period 2
(2007-2010)
Period 3
(2010-2012)
Period 4
(2012-2015)
10
8
6
4
2
0
30
-D
ay
P
os
td
is
ch
ar
ge
M
or
ta
lit
y,
%
Study Periods
Acute myocardial infarctionB
HRRP
announcement
HRRP
implementation
Period 1
(2005-2007)
Period 2
(2007-2010)
Period 3
(2010-2012)
Period 4
(2012-2015)
10
8
6
4
2
0
30
-D
ay
P
os
td
is
ch
ar
ge
M
or
ta
lit
y,
%
Study Periods
PneumoniaC
HRRP
announcement
HRRP
implementation
Period 1
(2005-2007)
Period 2
(2007-2010)
Period 3
(2010-2012)
Period 4
(2012-2015)
Readmission and death
Death and no readmission
Aggregate death
Readmission and death
Death and no readmission
Aggregate death
Readmission and death
Death and no readmission
Aggregate death

Trends in inverse probability-weighted overall 30-day postdischarge mortality
and 30-day postdischarge mortality stratified by whether there was an
associated readmission. Given the large sample size, CIs for all point estimates

were narrow and therefore not depicted (eg, overall mortality for heart failure in
period 1 was 8.3% [95% CI, 8.2%-8.4%]).

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Association of the Hospital Readmissions Reduction Program With Heart Failure, AMI, and Pneumonia Mortality Original Investigation Research

jama.com (Reprinted) JAMA December 25, 2018 Volume 320, Number 24 2549

© 2018 American Medical Association. All rights reserved.

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than incentives to improve mortality through pay-for-
performance programs, and some hospitals may have fo-
cused more resources and efforts on reducing or avoiding re-
admissions than on prioritizing survival. Studies have found
little evidence that standard measures of care quality for acute
myocardial infarction and heart failure are correlated with re-
admission rates,27,28 suggesting that as hospitals face choices
about which quality improvement efforts to prioritize, read-
missions could be at odds with other goals. Safety net hospi-
tals and hospitals serving a high proportion of socioeconomi-
cally disadvantaged patients were more likely to receive
financial penalties under the HRRP, potentially impeding their
ability to invest limited resources toward quality improve-
ment efforts to better outcomes.29-32 In addition, the HRRP may
have pushed some physicians and institutions to increas-
ingly treat patients who would have benefited from inpatient
care in emergency departments or observation units, which
could be consistent with the finding that increases in postdis-
charge mortality for heart failure and pneumonia were en-
tirely driven by patients who were not readmitted within 30

days of discharge. This is also in line with analyses that have
shown that following the HRRP, inpatient readmissions de-
clined while emergency department and observation unit stays
increased among patients returning to a hospital within 30 days
for target conditions.33

Alternatively, factors unrelated to the HRRP could poten-
tially explain the observed increases in postdischarge mortal-
ity. Greater use of hospice care at the end of life might shift
deaths that previously occurred within a hospital to the post-
discharge setting over time.21,22 However, increases in aggre-
gate death and death without readmission were similar even
after excluding patients receiving hospice care, indicating
that these trends were not explained by greater use of hos-
pice after hospital discharge. Increases in mortality after the
announcement and implementation of the HRRP could
potentially reflect greater use of do-not-resuscitate orders
among hospitalized beneficiaries. In a sample of hospitals in
California, for example, the proportion of do-not-resuscitate
orders among patients hospitalized for heart failure increased
over time.34 If these patterns were similar on a national scale,

Figure 4. Inverse Probability-Weighted 45-Day Postadmission Mortality for Target Conditions Before and After the Announcement
and Implementation of the Hospital Readmissions Reduction Program (HRRP)

16
14

12

10
8
6
4
2
0

45
-D

ay
P

os
ta

dm
is
si
on
M
or
ta
lit
y,
%
Study Periods
Heart failureA
HRRP
announcement
HRRP
implementation
Period 1
(2005-2007)
Period 2
(2007-2010)
Period 3
(2010-2012)
Period 4
(2012-2015)
16
14
12
10
8
6
4
2
0
45
-D
ay
P
os
ta
dm
is
si
on
M
or
ta
lit
y,
%
Study Periods
Acute myocardial infarctionB
HRRP
announcement
HRRP
implementation
Period 1
(2005-2007)
Period 2
(2007-2010)
Period 3
(2010-2012)
Period 4
(2012-2015)
16
14
12
10
8
6
4
2
0
45
-D
ay
P
os
ta
dm
is
si
on
M
or
ta
lit
y,
%
Study Periods
PneumoniaC
HRRP
announcement
HRRP
implementation
Period 1
(2005-2007)
Period 2
(2007-2010)
Period 3
(2010-2012)
Period 4
(2012-2015)

Trends in inverse probability-weighted 45-day postadmission mortality for
(A) heart failure, (B) acute myocardial infarction, and (C) pneumonia.

Given the large sample size, CIs for all point estimates are very narrow and
therefore not depicted.

Research Original Investigation Association of the Hospital Readmissions Reduction Program With Heart Failure, AMI, and Pneumonia Mortality

2550 JAMA December 25, 2018 Volume 320, Number 24 (Reprinted) jama.com

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trends in mortality might simply reflect greater focus on and
attention to goals of care among hospitalized patients or on
patients with advanced heart failure increasingly declining
life-prolonging care after discharge. It is also possible that the
overall increase in postdischarge mortality for heart failure
reflects inc reasing severity of illness among admitted
patients that is not captured in claims data. In incentivizing
hospitals to not admit patients, the HRRP might have been
associated with a change in patients who reached the thresh-
old of admission, resulting in the healthiest portion of these
encounters to be managed in the emergency department and
observation units and leaving an increasingly higher risk
population to be managed in the inpatient setting. Such a
shift, if uncaptured in claims, could have led to an increase in
mortality after hospitalization for heart failure. In contrast,
for pneumonia, recent evidence suggests that shifts in coding
practice may have resulted in a healthier cohort of patients
over time, because hospitals have increasingly recoded
severely ill patients with pneumonia to sepsis or respiratory
failure with pneumonia.35,36 Such shifts in coding make the
observed increase in postdischarge mortality among patients
with pneumonia less likely to be due to increases in unmea-
sured disease severity.

The current study builds upon a body of evidence regard-
ing the intended and potential unintended consequences of
the HRRP amid recent calls to restructure and improve the
program.5,30,37 Previous work has shown mixed findings re-
garding the relationship between the HRRP and mortality. A
report by the Medicare Payment Advisory Commission dem-
onstrated declines in risk-adjusted mortality since 2008 for all
target conditions,33 which was inconsistent with a number of
past analyses that have demonstrated an increase in heart fail-
ure and pneumonia mortality rates over the same period.17-19,38

A 2018 study showed no significant association between the
HRRP and increased mortality for target conditions.39 A third
investigation observed a weakly positive correlation be-
tween the HRRP and monthly changes in readmissions and
postdischarge mortality at the hospital level for all target
conditions.17 Although hospitals that reduce readmissions also
appear to reduce mortality, this hospital-level concordance
does not reflect the change in readmissions and mortality at

the level of the patient population, which is arguably of greater
importance to individual patients and to public health. The cur-
rent analysis is unique in that all Medicare inpatient claims data
were used to examine both postadmission and postdischarge
mortality at the patient level, stratified outcomes were evalu-
ated to provide mechanistic insights, and an IPW approach was
used to compare outcomes among similar patient popula-
tions in exposure periods before and after the announcement
and implementation of the HRRP.

Limitations
This study has several limitations. First, given the observa-
tional design, we are unable to make inferences about causal-
ity or the mechanisms that explain the increase in mortality
associated with the HRRP for some target conditions. Never-
theless, we attempted to account for secular trends in mortal-
ity using baseline years during which the HRRP was not in ef-
fect, making it unlikely that observed associations between the
HRRP and mortality were due to preexisting trends alone. Sec-
ond, patient severity of illness may have differed in ways that
were not captured by claims data. But, to minimize confound-
ing, we used inverse probability weighting, an approach that
is less susceptible to biased estimates of the HRRP’s associa-
tion with mortality due to imbalances in covariates over time.
Third, recent studies have demonstrated up-coding associ-
ated with the HRRP, although such changes would have
attenuated the observed relationship between the HRRP and
increased mortality.40

Conclusions
Among Medicare beneficiaries, announcement and imple-
mentation of the HRRP were significantly associated with an
increase in 30-day postdischarge mortality following hospi-
talization for heart failure and pneumonia, but not for acute
myocardial infection. Given the study design and the lack of
significant association of the HRRP implementation with mor-
tality within 45 days of hospital admission, further research
is needed to understand whether the increase in 30-day post-
discharge mortality is a result of the HRRP.

ARTICLE INFORMATION

Author Affiliations: Richard A. and Susan F. Smith
Center for Outcomes Research in Cardiology,
Division of Cardiology, Beth Israel Deaconess
Medical and Harvard Medical School, Boston,
Massachusetts (Wadhera, Shen, Yeh); Brigham and
Women’s Hospital Heart & Vascular Center, Harvard
Medical School, Boston, Massachusetts (Wadhera);
Cardiovascular Division, Department of Medicine,
Washington University School of Medicine, St Louis,
Missouri (Joynt Maddox); Cardiology Division,
Department of Medicine, Massachusetts General
Hospital, Harvard Medical School, Boston,
Massachusetts (Wasfy); Department of
Biostatistics, Harvard T.H. Chan School of Public
Health, Boston, Massachusetts (Haneuse).

Author Contributions: Drs Wadhera and Yeh had
full access to all the data in the study and take

responsibility for the integrity of the data and the
accuracy of the data analysis.

Conflict of Interest Disclosures: The authors have
completed and submitted the ICMJE Form for
Disclosure of Potential Conflicts of Interest.
Dr Wadhera is supported by National Institutes of
Health Training grant T32HL007604-32, and
previously served as a consultant for Regeneron.
Dr Joynt Maddox receives research support from
the National Heart, Lung, and Blood Institute
(K23HL109177-03) and provides contract work for
the US Health and Human Services. Dr Wasfy
receives research support from the National
Institutes of Health KL2 Grant (TR001100) and
American Heart Association (18CDA34110215).
Dr Yeh receives research support from the National
Heart, Lung, and Blood Institute (R01HL136708)
and the Richard A. and Susan F. Smith Center for
Outcomes Research in Cardiology and received

grants and personal fees from Abbott Vascular,
grants from Abiomed, personal fees from Asahi
Intecc, grants from AstraZeneca, grants and
personal fees from Boston Scientific, personal fees
from Medtronic, and personal fees from Teleflex
outside the submitted work. The other authors
report nothing to disclose.

Funding/Support: This work was supported by the
Richard A. and Susan F. Smith Center for Outcomes
Research in Cardiology.

Role of the Funder/Sponsor: The funders had no
role in the design and conduct of the study;
collection, management, analysis, and
interpretation of the data; preparation, review, or
approval of the manuscript; and decision to submit
the manuscript for publication.

Association of the Hospital Readmissions Reduction Program With Heart Failure, AMI, and Pneumonia Mortality Original Investigation Research

jama.com (Reprinted) JAMA December 25, 2018 Volume 320, Number 24 2551

© 2018 American Medical Association. All rights reserved.

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Research Original Investigation Association of the Hospital Readmissions Reduction Program With Heart Failure, AMI, and Pneumonia Mortality

2552 JAMA December 25, 2018 Volume 320, Number 24 (Reprinted) jama.com

© 2018 American Medical Association. All rights reserved.

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Aiming for Fewer Hospital U-turns: The Medicare Hospital Readmission Reduction Program

Aiming for Fewer Hospital U-turns: The Medicare Hospital Readmission Reduction Program

Aiming for Fewer Hospital U-turns: The Medicare Hospital Readmission Reduction Program

Aiming for Fewer Hospital U-turns: The Medicare Hospital Readmission Reduction Program

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Th

e

purpos

e

of

this initial paper is to briefly describe your search strategies when identify

ing

two articles

that pertain to an evidence

–

based practice to

pic

of interest

. Mine is on

A

voiding Hospital

Readmissions. I

will be focusing as an individual on

e

xamin

ing

the sources of knowledge that contribute to professional

nursing practic

e

qualitative or quantitative design?

A

pply research principles to the interpretation of the

content of published research studies.

“What is the number of

 

trends in 30

–

day post

–

discharge mortality

among beneficiaries after the implementation of HRRP

—

period 3 and 4, for mortality rate in myocardial

infarctions?” (Wadhera, et al., 2018)

 

Clinical Question

:

A

. Describe problem

b. Significance of problem in terms of outcomes or statistics

c. Your PICOT question in support of the group to

pic

d. Purpose of your paper

B

.

Levels of Evidence

a. Type of question asked

b. Best evidence found to answer question

C.

Search Strategy

a. Search terms

b. Databases used (you may use Google Scholar in addition to the library databases; start with the

Lib

rary)

c. Refinement decisions made

d. Identification of two most relevant articles

D.

Format

a. Correct grammar and spelling

b. Use of headings for each section

c. Use of APA format (sixth edition)

d. Page length: three to four pages

Clinical Question

45 points 28%1. Problem is described. What is the focus of your group’s work? 2. Significance of the problem is

described. What health outcomes result from your problem? Or what statistics document this is a

problem? You may find support on websites for government or professional organizations. 3. What is

your PICOT question? 4. Purpose of your paper. What will your paper do or describe? This is similar to a

problem statement. “The purpose of this paper is to . . .”

Levels of Evidence

20 points 13% 1. What type of

question are you asking (therapy, prognosis, meaning, etc.)? 2. What is the best type of evidence to be

found to answer that question (e.g., RCT, cohort study, qualitative study)?

Search Strategy

65 points 41% 1.

Search topic(s) provided. What did you use for search terms? 2. What database(s) did you use? Link your

search with the PICOT question described above. 3. As you did your search, what decisions did you make

in refinement to get your required articles down to a reasonable number for review? Were any limits

used? If so, what? 4. Identify the two most relevant and helpful articles that will provide guidance for

your next paper and the group’s work. Why were these two selected?

Format

30 points 18% 1. Correct grammar

and spelling 2. Use of headings for each section: Clinical Question, Level of Evidence, Search Strategy,

Conclusion 3. APA format (sixth ed.) 4. Paper length: three to four pages

Total worth 160 points

DIRECTIONS AND ASSIGNMENT CRITERIA Assign ment Criteria Points % Description Clinical Question 45

28 1. Problem is described. What is the focus of your group’s work? 2. Significance of the problem is

described. What health outcomes result from your problem? Or what statistics document this is a
problem? You may find support on websites for government or professional organizations. 3. What is
your PICOT question? 4. Purpose of your paper. What will your paper do or describe? This is similar to a
problem statement. “The purpose of this paper is to .

. .” Levels of Evidence 20 13 1. What type of
question are you asking (therapy, prognosis, meaning, etc.)? 2. What is the best type of evidence to be

found to answer that question (e.g., RCT, cohort study, qualitative study)? Search Strategy 65 41 1.

Search topic(s) provided. What did you use for search terms? 2. What database(s) did you use? Link your
search with the PICOT question described above. 3. As you did your search, what decisions did you make

in refinement to get your required articles down to a reasonable number for review? Were any limits

used? If so, what? 4. Identify the two most relevant and helpful articles that will provide guidance for

your next paper and the group’s work. Why were these two selected? Format 30 18 1. Correct grammar

and spelling 2. Use of headings for each section: Clinical Question, Level of Evidence, Search Strategy,

Conclusion 3. APA format (sixth ed.) 4. Paper length: three to four pages

The purpose of
this initial paper is to briefly describe your search strategies when identifying two articles
that pertain to an evidence
–
based practice topic of interest
. Mine is on
Avoiding Hospital
Readmissions. I
will be focusing as an individual on
e
xamin
ing

the sources of knowledge that contribute to professional
nursing practic
e
qualitative or quantitative design?

A
pply research principles to the interpretation of the
content of published research studies.
“What is the number of

trends in 30
–
day post
–
discharge mortality
among beneficiaries after the implementation of HRRP
—

period 3 and 4, for mortality rate in myocardial
infarctions?” (Wadhera, et al., 2018)

Clinical Question
:

A
. Describe problem

b. Significance of problem in terms of outcomes or statistics

c. Your PICOT question in support of the group to
pic

d. Purpose of your paper

B
. Levels of Evidence

a. Type of question asked

b. Best evidence found to answer question

C.

Search Strategy

a. Search terms

b. Databases used (you may use Google Scholar in addition to the library databases; start with the
Lib
rary)

c. Refinement decisions made

d. Identification of two most relevant articles

D.

Format

a. Correct grammar and spelling

b. Use of headings for each section

c. Use of APA format (sixth edition)

The purpose of this initial paper is to briefly describe your search strategies when identifying two articles

that pertain to an evidence-based practice topic of interest. Mine is on Avoiding Hospital Readmissions. I

will be focusing as an individual on examining the sources of knowledge that contribute to professional

nursing practice qualitative or quantitative design? Apply research principles to the interpretation of the

content of published research studies. “What is the number of trends in 30-day post-discharge mortality

among beneficiaries after the implementation of HRRP — period 3 and 4, for mortality rate in myocardial

infarctions?” (Wadhera, et al., 2018)

Clinical Question:

A. Describe problem

b. Significance of problem in terms of outcomes or statistics

c. Your PICOT question in support of the group topic

d. Purpose of your paper

B. Levels of Evidence

a. Type of question asked

b. Best evidence found to answer question

C. Search Strategy

a. Search terms

b. Databases used (you may use Google Scholar in addition to the library databases; start with the

Library)

c. Refinement decisions made

d. Identification of two most relevant articles

D. Format

a. Correct grammar and spelling

b. Use of headings for each section

c. Use of APA format (sixth edition)