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 ManagementBrandeis University

Christopher Tompkins. Ph.D.Grant Ritter, Ph.D.

Robert Mechanic, M.B.A.Jennifer Perloff, Ph.D.John Chapman, Ph.D.

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.

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.

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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 one-third 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).

1 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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Estimated Distribution of Gains and Losses by Case Volume

Gain/(Loss) for Total Joint Replacement 30-49 50-74 75-99 100-124

Top 5% $5,947 $5,113 $3,513 $2,924

Bottom 5% -24.0% -19.1% -17.5% -11.6%

Mean TJR $24,394 $24,394 $24,394 $24,394

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.

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

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Unadjusted Data

Gain/(Loss) for Combination of Episodes by Case Volume100 - 249 250 - 499 500 - 749 750 - 999

5th ($446,500) ($682,518) ($985,582) ###

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 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.

2 Further detail is available at http://www.hci3.org/content/cms-cmmi-bundled-payments-care-initiative-pilot-resources

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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 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

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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

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

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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-5Impact 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.

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

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Median 0.8% -0.6% -0.1% 0.3%75th 5.6% 2.9% 2.5% 2.5%95th 13.4% 9.4% 6.8% 6.4%

Severity Adjustment and Stop-loss at 95th percentile of Cost

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 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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