Brandeis University
The Heller School for Social Policy and Management
Analysis of Financial Risk and Risk
Mitigation Option in the Medicare Bundled
Payment For Care Improvement Program
Draft for Discussion
June 28, 2012
Prepared by
The Schneider Institutes for Health Policy
The Heller School for Social Policy and Management
Brandeis University
Christopher Tompkins. Ph.D.
Grant Ritter, Ph.D.
Robert Mechanic, M.B.A.
Jennifer Perloff, Ph.D.
John Chapman, Ph.D.
Brandeis University has provided analytic support to hospitals applying to participate in
the new CMS Bundled Payment for Care Improvement Program. Most of these hospitals
have focused on episodes that include both acute and plus post-acute services (Model 2).
We prepared this white paper to describe sources of financial risk in the bundled payment
program and to assess potential risk mitigation strategies. Important financial risks
include spending for services that are outside the scope of care redesign, systematic
changes in patient severity between the baseline period through the duration of the
demonstration and random year-to-year variation in providers’ average episode costs
since many hospitals have relatively modest episode case volumes. We have modeled the
impact of exclusions, severity adjustment and stop loss protection on hospital risk. In the
coming months CMS will make decisions about program design and begin negotiating
with hospitals that it selects to participate in the program. The purpose of this white paper
is to inform these discussions by summarizing recent and ongoing work that Brandeis
University has undertaken to evaluate ways to mitigate risk without adding additional
costs or undesirable incentives into the program. Further technical details about the
summary analyses provided in this paper are available from the authors.
In August 2011, the Center for Medicare and Medicaid Innovation (CMMI) released a
request for applications for its new Bundled Payment for Care Improvement
Demonstration Program. Under the demonstration, hospitals will assume financial risk
for delivering services to Medicare beneficiaries for defined episodes of care. CMS
provided applicants with the option to participate in four distinct models for the
demonstration. Model 1 bundles hospital care and professional services during an
inpatient admission. Model 2 bundles hospital care, professional services, and post-acute
care for the admission and for a period of 30 – 180 days after discharge. Model 3 bundles
post-acute care services following an admission. Each of these models will be
administered on a “retrospective” basis in which CMS will pay medical claims as they
occur and conduct periodic reconciliations of target and actual spending levels. CMS also
offers a Model 4 that will make prospective payment covering hospital care and
professional services during an admission plus any readmissions within 30 days.
The bundled payment demonstration provides new financial incentives for hospitals and
health systems to improve the efficiency, quality and coordination of patient care within
defined clinical episodes. In each of the “retrospective” models, CMS will establish a
“target price” based on each applicant’s historical costs. CMS will update the historical
target prices to the start of the demonstration program (e.g., 2013) less a required
“discount” that each hospital must propose in its application. CMS will then compare the
actual expenditures for the episodes each hospital has selected during the performance
period to the hospital’s target prices. If actual expenditures are below the target price
applicants will be paid the surplus. If actual expenditures are above the target price
applicants must return the excess amount to CMS. In Model 4, hospitals will receive a
fixed prospective payment per episode with no payment reconciliation.
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In their applications, hospitals must identify the model they want to apply for, define
specific episodes (e.g., congestive heart failure, total joint replacement, stroke etc.)
including the relevant DRGs, episode length, and services that they propose should be
excluded from the episodes (e.g., readmissions and services designated by ICD-9 codes)
that are not considered clinically relevant to the principal episode. Hospital applicants
are, however, required to include all patients that are admitted for the designated DRGs in
each episode when they calculate the target price and their actual performance.1
Brandeis University has developed and analyzed episodes for over a hundred hospitals
interested in applying for the Bundled Payment program. We have worked with three
Convener organizations: Geisinger Health System; Association of American Medical
Colleges; and the Estes Park Institute as well as two health systems: Partners Healthcare
and Fairview Health Services. Our analysis has included data from approximately onethird of the nation’s hospital referral clusters (HRCs) and it has provided significant
insight into both the opportunities and risks facing hospitals that participate in this
demonstration. Our analysis has also provided insights about steps CMS could take to
reduce some of the risk facing hospitals that we believe would contribute significantly to
the potential for a successful program. The majority of hospitals that we have worked
with have focused on Model 2 with a post-discharge episode time window of 90 days.
Therefore this white paper is devoted to an analysis of risk and risk mitigation under
Model 2. However, our general findings would also apply to Model 3 and to a lesser
extent to the other models. Our key conclusions are summarized below.
1. Hospitals face significant financial risks due to random variation in average
severity of patients within specific episodes because of low case volumes.
A principal source of risk for hospitals participating in this demonstration is the
random variation in year-to-year average episode costs that are driven by the
random variation in the severity of patient that are treated within any given annual
period. In contrast to ACOs, which much have at least 5,000 patients to participate in
Medicare’s Shared Savings Program, the hospitals applying for the bundled payment
program commonly have between 50 and 200 cases in their highest volume episodes.
Hospitals that happen to treat a larger number of high-cost outlier cases in the
performance year that were not present in the historical period data that were used to set
the target price could lose thousands of dollars per episode. Such losses could swamp
any improvements in efficiency gained from clinical performance improvement and
care coordination.
Table ES-1 summarizes a simulation of the impact of random variation in patient mix
changes in year-to-year target prices between 2008 and 2009 using historical claims data
provided by CMS. The analysis profiles the distribution of per-episode gains and losses
due to random variation for 90-day episodes for total joint replacement (TJR).
Medical group applicants are responsible for all patients admitted by their
physicians regardless of the specific hospital where the admission takes place.
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Table ES-1: Gains and Losses Due to Random Variation by Hospital Case Volume
for 90-Day Total Joint Replacement Episode
The magnitude of random gains and losses are greatest in hospitals with lower case
volumes. The table illustrates that five percent of hospitals with fewer than 100 cases
would face losses of 17 – 24 percent per case ($4000 - $6,000) solely due to random
variation. Twenty-five percent of these smaller hospitals would lose at least 5 – 8 percent
at least $1,800 - $2,100 per case. The losses are smaller but still significant for larger
hospitals. For hospitals with 100 – 200 TJR cases, five percent would lose 11 percent per
case ($2,500 - $2,800) and twenty five percent would lose at least 4 – 5 percent per case
($800 – $1,300).
Losses would be greater for CHF episodes in which there is substantially more cost
variation than for TJR (see Table ES-2). Losses for the bottom five percent of hospitals
range from 40 percent for the hospitals with 30 – 49 cases to 19 percent for hospitals with
with 125 – 149 CHF cases. The range for hospitals at the bottom 25th percentile is from 9
– 13 percent.
Table ES-2: Gains and Losses Due to Random Variation by Hospital Case Volume
for 90-Day Congestive Heart Failure Episode
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In addition to losses, random variation will also generate large financial gains for
hospitals in the demonstration. Thus random variation can lead to two different
undesirable outcomes. Hospitals that implement significant clinical performance
improvements can incur financial losses if they are negatively affected by random
variation and hospitals that do not improve clinical performance may nonetheless
generate gains if they are positively affected by random variation.
Hospitals that enter the demonstration with multiple episodes can reduce the overall
impact of random variation across all of their cases. For example, a hospital that selects
four episodes may see random variation in one episode offset by random variation in
another. Although this approach reduces risk on a per-case basis, selecting more episodes
for the demonstration increases each applicants total dollar risk exposure. This is
illustrated in Table ES-3. Although hospitals in the bottom five percent of hospitals with
1,250 – 1,499 would lose substantially less as a percentage (about 4 percent) than would
a hospital with 100 – 250 cases (about 11 percent loss). But the hospital with larger
volume would face a higher overall risk exposure ($1,164,570) than the smaller
institution ($446,500).
Table ES-3: Total Gains and Losses Due to Random Variation Across Multiple
Episodes by Hospital Case Volume
* Figures based on a weighted average of spending for 9 different episodes.
Gains and losses due to random variation would also be mitigated by hospital’s
participation in the program over multiple years as potential random gains or losses in
one year, would be offset by those in subsequent years. The hospitals applying for this
demonstration have expressed interest in moving towards new models of payment and
delivery of care and many are embarking on this effort as part of a long-term
transformation effort. Nonetheless some institutions may be discouraged from this
direction should they encounter large short-term financial losses.
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2. CMS should consider multiple options to mitigate the risk created by
random variation.
The objective of this demonstration is to reward hospitals for improvements in clinical
performance that lead to improved quality of care for Medicare beneficiaries and lower
overall spending. However, the significant financial risk created by random variation in
patient care costs has the potential to diminish this ability of the demonstration to meet
this objective. We believe that the presence of reasonable risk mitigation approaches
within the program design will be a central factor considered by program applicants prior
to committing to proceed with the program. We believe adequate risk mitigation will be
important to the ultimate success of this demonstration, particularly if CMS wants to
encourage a diverse pool of program participants. We discuss several different risk
mitigation strategies.
a. Exclusions
CMS allows hospitals to propose exclusions for specific readmissions and Part A and
Part B services defined by ICD-9 diagnosis codes. These exclusions allow hospitals to
remove services from the episode that are not clinically related to the index admission
and cannot be managed or controlled by treating clinicians. For example, few hospitals
would want to bear risk for trauma services as part of an episode to treat congestive heart
failure because the occurrence of trauma is not related to the patient’s CHF nor is it
preventable by treating clinicians.
We modeled the impact of three approaches to exclusions to determine their impact on
variation in average episode costs: 1) no exclusions; 2) a limited set of exclusions related
to metastatic cancer, organ transplant, and several other costly complex illnesses, and 3) a
standard exclusion set that modified the recommended exclusion lists produced by HCI3.
Our analysis determined that these exclusions reduced the mean cost for specified
episodes but had very little impact on the extent of variation for either CHF or CABG
episodes. Therefore we concluded that the exclusion approaches allowed under the
demonstration do not meaningfully address the problem of random variation. Others have
suggested that CMS could substantially reduce variation by allowing hospitals to exclude
patients with selected sets of primary diagnoses from the episode definitions.2 However,
we have not modeled such an approach for this white paper.
b. Stop-loss protection
A major contributor to the random variation in episode costs is the existence of ‘high
cost’ or outlier cases. The presence of a few outlier cases can skew a hospital’s cost
distribution and undermine its ability to compute fair target prices. CMS has
acknowledged the problem of outlier costs in other programs including inpatient hospital
Further detail is available at http://www.hci3.org/content/cms-cmmi-bundledpayments-care-initiative-pilot-resources
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DRGs and the Medicare Shared Savings program. CMS would be well served to consider
similar approaches for bundled payments.
Setting a stop loss limit at the 90 – 95th percentile would reduce the year-to-year random
variation in average episode costs, but it would reduce average episode prices across
participating hospitals, making it essentially budget neutral to CMS. For example,
based on our data set, stop-loss protection at the 95th percentile for 90-day CHF episodes
would limit a hospital’s episode cost (measured against its target price) to about $47,000.
With stop loss protection a hospital with a $70,000 case would only have $47,000
assigned against its target price. Since outlier cases would be removed from both the
target price calculation and the actual performance measurement, CMS would not occur
additional costs, while providing additional protection for demonstration participants.
There are multiple ways that stop loss protection could be structured. For the models in
this paper, we have calculated stop-loss levels based on all hospitals in our analytic
database. An important concern with this approach is that it would provide more limited
protection for smaller hospitals with less complex patients. For example, stop loss levels
calculated based on all hospital costs in an HRC with many large teaching hospitals
would provide little protection for smaller community hospitals with less complex
patients. Therefore stop-loss levels could be calculated based on a cohort of similar
hospitals (e.g., considering size, teaching status, region, case-mix index). Another option
would be calculate stop loss thresholds for individual hospitals in the demonstration
based on several years of historical data.
The rationale for providing stop-loss protection is to focus demonstration participants on
developing clinical interventions to effectively manage spending under normal
circumstances rather being distracted or undermined by losses that could occur due to
random variation in their high cost outlier cases.
Table ES-4 illustrates the impact of stop loss protection at the 95th percentile on total risk
exposure for hospitals that participate in multiple episodes. The table provides analysis
for hospitals that have 500 – 750 cases across nine different episodes. The total risk
exposure for hospitals with losses at the 5th percentile compared to their target price is
estimated to be about $986,000. With stop loss at the 95th percentile the total estimated
risk exposure is reduced by about 10 percent to about $891,000. Stop loss has a relatively
modest impact on losses for hospitals at the 25th percentile. While stop loss at the 95th
percentile clearly reduces risk – the total risk exposure remains high. In order to reduce
risk exposure further CMS could adopt more aggressive stop loss protection (e.g., stop
loss at the 90th percentile) or could combine stop loss with other risk mitigation
techniques like risk adjustment.
c. Risk adjustment
Because this demonstration uses historical data to establish target prices for episodes,
applicant hospitals are concerned that that there could be significant change in the
severity of patients they treat for particular episodes between the base period and the
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performance year. This could occur due to increased use of observation stays,
introduction of new technologies, expansion of specific departments within a hospital, or
simply random change in patient referral patterns. One approach to reducing the impact
of such changes would be to include adjustments based on changes in patient severity
into the calculation of the episode price. We have developed such a risk adjustment
model using regression analysis to predict individual patients’ episode costs for each nine
discrete episode categories. The dependent variable in our model is total episode cost and
the independent variables include: patient demographics, co-morbidities observed during
the 90 days prior to admission, and patient severity measures (diagnosis-based) during
the inpatient stay itself.
In recommending severity adjustment, it is important to point out what it will and will not
do. It will pick up changes in severity due to greater use of observational stays and
systematic shifts in the severity of patients treated at participating hospitals. But it will
not adjust for the use of new, higher cost procedures or treatments within a particular
DRG or episode.
Table ES-3 demonstrates the impact of our proposed risk adjustment model on total risk
exposure for hospitals with 500 – 750 cases across nine different episodes. The total risk
exposure for hospitals with losses in the 5th percentile compared to their target price is
estimated to be about $986,000. Risk adjustment reduces their total estimated risk
exposure by about 6 percent to about $923,000. The risk adjustment model actually
increases risk exposure slightly hospitals at the 25th percentile. Therefore, while risk
adjustment will benefit hospitals in the bundled payment demonstration, its overall
impact on risk exposure is very modest.
d. Combining Stop-Loss Protection and Risk adjustment
Combining risk adjustment and stop loss protection at the 95th percentile has a greater
impact on lowering hospitals’ risk exposure. Table ES-3 illustrates the impact of stop loss
protection at the 95th percentile on total risk exposure for hospitals that participate in
multiple episodes. A hospital with 500 – 750 cases would reduce its total risk exposure at
the 5th percentile by nearly 20 percent from $986,000 to $784,000. Stop loss combined
with risk adjustment reduces expected losses for hospitals at the 25th percentile by more
than 30 percent from $353,000 to $241,000.
Table ES-4:
Impact of Stop-loss and Risk Adjustment on Total Risk Exposure Due to Random
Variation For Hospital With 500 – 750 Cases Across a Variety of Episodes
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Table ES-5 provides a summary of the range of risk exposure for hospitals assuming that
all of the risk mitigation techniques discussed above are implemented. Hospital risk
exposure is lower with both stop-loss and risk adjustment. Potential losses for hospitals
with 100 – 250 cases are about 10 percent at the 5th percentile and drop to 3 percent at the
25th percentile. For hospitals with larger case volume the risk exposure drops to 3 to 6
percent at the 5th percentile and 2 to 3 percent at the 25th percentile. Ultimately it is up
individual hospitals to interpret the extent of risk exposure they are willing to accept on
top of the Demonstration’s required discounts. Nonetheless risk mitigation should
increase of hospitals’ comfort level that they will be rewarded financially for improving
clinical performance.
Table ES-5
Impact of Stop-loss and Severity Adjustment on Total Risk Exposure Due to
Random Variation Across Multiple Episodes by Hospital Case Volume
* Calculations assume average volume by category and average episode cost of $22,500.
e. DRG smoothing
The CMS requirement that hospitals establish separate prices for individual DRGs within
each episode and then combine the prices to create the overall episode target price is
likely to amplify year-to-year cost variation. Some hospitals with very low DRG volumes
within an episode in the base year will generate DRG prices that are far different than the
average cost.
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One solution to this problem is to smooth DRG-specific prices through a multi-step
process: first, establish the overall episode price for each hospital based on an acceptable
methodology; second, run a regression model on a nationally representative sample of
patients within the episode, which include all DRGs within the episode as covariates; and
third, use the regression-based estimates of DRG costs to establish relative DRG prices
that aggregate to the correct composite episode price at the hospital. While budget neutral
overall, this ‘smoothing’ process should attenuate much of the DRG price variation that
hospitals will experience due to small DRG samples.
f. Additional risk mitigation options for consideration
Hospitals that choose to participate in the CMS Bundled Payment Demonstration are
doing so because of the expectation that Medicare payment will increasingly move
towards models that hold them accountable for the quality and value of the care they
deliver to patients within logical constructs like episodes of care. These hospitals are
embracing new initiatives to improve their clinical performance that will help them to
succeed in these new models. However as we demonstrate in this white paper,
participating hospitals face significant risk of random variation in year-to-year episode
costs that will lead to losses for some of them – irrespective of their success in improving
clinical performance. This risk can be mitigated somewhat by adjustments to the method
of calculating episode prices. This paper presents four such approaches that are budget
neutral and would reduce but not eliminate random variation in episode cost without
substantially affecting how hospitals respond to the demonstration’s incentives. The
options are independent of each other so that any could be applied without undermining
the impact of another.
While the approaches presented in this paper somewhat mitigate the impact of random
variation in year-to-year episode costs, significant variation remains. As illustrated above,
even with exclusions, stop loss protection, and severity adjustment, there is a 25 percent
chance that hospitals will incur losses of 2 – 4 percent and a 5 percent chance they will
incur losses of 3 – 10 percent depending on overall volume due solely to random
variation. These amounts along with the required 2 – 3 percent discount represent a
financial hurdle for participating hospitals.
It would be unfortunate if the prospect of short-term losses were to discourage applicants
from the program in its early stages. One approach to preventing this would be to adopt
much stronger risk mitigation tools in the early stages of the program, which could be
phased out over time as participants develop and refine their performance improvement
strategies and become more comfortable with bundled payments. One option would be
for CMS to allow hospitals to share both savings and losses with it on a 50/50 basis –
thus reducing risks due to random variation by half. A second option would be lower the
initial thresholds for stop loss protection to the 70th or 80th percentile in the programs
initial years and gradually raise the threshold over time. A third option would be to
require hospitals to offer a fixed discount off of the historical target price and the
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opportunity to retain gains beyond the discount – but hold the hospitals harmless for
losses beyond the discount.
The approaches described in this paper are not exhaustive and there are other options
could conceivably add significant benefit for the Demonstration by further reducing
random variation. Given the large observed variation, even after applying the
abovementioned strategies, we believe that it is incumbent on CMS to continue exploring
options. We would be pleased to assist CMS and participating hospitals in the coming
months by simulating the potential impacts of additional options.
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