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How Will Provider-Focused Payment Reform Impact Geographic Variation in Medicare Spending?
David Auerbach, PhD, MS; Ateev Mehrotra, MD, MPH; Peter Hussey, PhD; Peter J. Huckfeldt, PhD; Abby Alpert, PhD; Christopher Lau, PhD; and Victoria Shier, MA
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How Will Provider-Focused Payment Reform Impact Geographic Variation in Medicare Spending?

David Auerbach, PhD, MS; Ateev Mehrotra, MD, MPH; Peter Hussey, PhD; Peter J. Huckfeldt, PhD; Abby Alpert, PhD; Christopher Lau, PhD; and Victoria Shier, MA
Unlike ACOs or P4P, implementation of bundled payment for inpatient and post acute care in Medicare would modestly reduce geographic variation in spending.

Objectives: The Institute of Medicine has recently argued against a value index as a mechanism to address geographic variation in spending and instead promoted payment reform targeted at individual providers. It is unknown whether such provider-focused payment reform reduces geographic variation in spending.

Study Design: We estimated the potential impact of 3 Medicare provider-focused payment policies—pay-for-performance, bundled payment, and accountable care organizations—on geographic variation in Medicare spending across Hospital Referral Regions (HRRs). We compared geographic variation in spending, measured using the coefficient of variation (CV) across HRRs, between the baseline case and a simulation of each of the 3 policies.

Methods: Policy simulation based on 2008 national Medicare data combined with other publicly available data.

Results: Compared with the baseline (CV, 0.171), neither pay-for-performance nor accountable care organizations would change geographic variation in spending (CV, 0.171), while bundled payment would modestly reduce geographic variation (CV, 0.165).

Conclusions: In our models, the bundled payment for inpatient and post acute care services in Medicare would modestly reduce geographic variation in spending, but neither accountable care organizations nor pay-for-performance appear to have an impact.

Am J Manag Care. 2015;21(6):e390-e398

Take-Away Points

Medicare spending per fee-for-service beneficiary in 2008 varied from $6000 in Rapid City, South Dakota, to more than $18,000 in Miami, Florida. While some policy makers favor directly reducing payments in high-cost areas, the Institute of Medicine favors policies focused on provider inefficiency. We investigated whether several such policies would have the virtue of also reducing variation in spending. If inefficiency is concentrated in high-cost areas, then this may be the case.
  • Robust pay-for-performance and rapid diffusion of accountable care organizations would have minimal impact on such variation.
  • Bundling inpatient and post acute care costs would modestly reduce such variation.
Medicare spending per fee-for-service (FFS) beneficiary in 2008 varied widely across the 306 Hospital Referral Regions (HRRs) of the United States: it ranged from $6000 in Rapid City, South Dakota, to more than $18,000 in Miami, Florida. Some of this geographic variation is driven by differing health status and other demographic differences among residents of these regions.1 However, research has also suggested that a large component of this geographic variation is due to differences in medical practice that do not appear to be associated with better healthcare quality or outcomes.2-4 These concerns have raised interest in policies aimed toward reducing geographic variation in spending.

As part of the debate over the Affordable Care Act, Congress considered implementing a “value index,” which would directly address this variation by reducing the rates Medicare pays providers in high-cost regions.5 However, the Institute of Medicine (IOM) has argued against such policies, deeming them an overly blunt instrument.6 Varying payment rates in this way would not account for the substantial differences within regions in provider efficiency, and thus, could penalize low-cost providers in high-cost regions.

Instead, the IOM favors policies that aim to reduce inefficiency at the provider level. Indeed, if inefficiency is more prevalent in high-cost areas, then policies that reduce inefficiency might also reduce geographic variation in spending as a beneficial accompanying effect. To assess the effects of such policies, the IOM asked us to model the impact of provider-focused interventions on geographic variation in spending.

In this article, we estimate the impact of 3 provider-focused policies on geographic variation in Medicare spending: 1) bundled payment, 2) pay-for-performance (P4P), and 3) accountable care organizations (ACOs). We chose these 3 policies as prominent, realistic interventions that are currently being implemented or piloted in Medicare, as well as in the private sector. Generally, these policies aim to improve upon the incentive inherent in FFS payment to increase volume of care without necessarily improving outcomes.

For each policy, we generated a number of scenarios representing realistic but robust implementations of the policy and then estimated geographic variation in Medicare spending under each scenario. To ensure that the scenarios would be realistic, we based their design on policies that Medicare has already implemented (either as pilots or full programs). To ensure that they are robust (ie, illustrative of the potential impacts of a large-scale program), we modified the existing programs in many key ways. For example, instead of a voluntary bundled payment program, as is currently being piloted in Medicare, we modeled a mandatory program. We provide an overview of the policies and scenarios below with additional details in the eAppendix (available at

How the 3 Policies Theoretically Could Decrease Geographic Variation

In this section, we explain potential mechanisms by which these policies could reduce geographic variation in spending. Under a P4P program, providers such as hospitals, medical groups, and nursing homes receive higher payments if they attain a high level of performance on quality measures, or improve their performance on quality measures (some related recent policies also reward performance on cost measures, but we did not include such scenarios). For P4P to decrease geographic variation in spending, there must be a cost-quality relationship. If high-quality providers are clustered in regions with lower spending, then the P4P program would shift money from high-cost areas to low-cost areas, thereby reducing geographic variation in spending.

“Bundled payment” is a payment method in which providers receive a single payment for so-called bundles of healthcare services related to a patient’s medical condition or a medical procedure. For bundled payment to decrease geographic variation in Medicare spending, there would need to be a national payment rate for each bundle and high-cost providers of bundles clustered geographically.

ACOs were initiated in Medicare as part of the Affordable Care Act. Organizations assume responsibility for the total costs of care for a designated population of Medicare beneficiaries, and if Medicare payments for assigned beneficiaries fall below a target, Medicare pays the provider organizations a fraction of the difference as bonus payments (if quality standards are met), and thus both Medicare and the organization benefit financially. In some models, provider organizations may also lose money if Medicare payments for assigned beneficiaries exceed the target. If ACOs do save money for Medicare on net and are clustered in higher-cost areas—and/or if ACOs save more in high-cost areas than low-cost areas—then geographic variation in spending could be reduced.


To evaluate whether the 3 policies would decrease geographic variation in Medicare spending, we compared 2008 Medicare spending for each HRR under the baseline case with scenarios in which the policy was implemented. We compared the degree of geographic variation in the baseline case with that under the policies. Given space limitations, we provide an overview of our work and only 1 scenario per policy. The online eAppendix includes a detailed description of methods and data as well as results of other scenarios (sensitivity analyses) for each policy.

We compared all scenarios with unadjusted total Medicare spending for FFS full-year Part A and Part B enrollees 65 years or older in 2008, as reported by the IOM.7 The underlying data derives from the CMS Chronic Conditions Warehouse,8 which contains all Medicare claims for FFS beneficiaries. Methods, key assumptions, and data unique to each of our policy scenarios are described below. We present each policy independently, though we recognize potential policy interaction with simultaneous implementation.


We analyzed the impact of Medicare P4P programs targeting hospitals, nursing homes, and home health agencies (we report the effects of all programs combined). We based scenarios on existing or pilot Medicare P4P initiatives—specifically, the Hospital Value-Based Purchasing Program, the Nursing Home Quality-Based Purchasing Demonstration, and the Home Health Pay-for-Performance Demonstration.

Reflecting the design of existing and prior P4P programs, we measured each provider’s performance on quality measures in terms of both achievement and improvement based on publicly available quality scores, with the latter based on changes in scores over 2 years. Using the 2008 total Medicare spending baseline, we estimated the effects of transferring 15% of total provider payments to an incentive pool. We then allocated pool funds to providers based on a linear exchange curve method in which the provider with the worst performance received no incentive payments, and providers received larger incentive payments with increased performance. Compared with the nursing home and home health Medicare programs currently being implemented, our scenarios were larger in scope (a national program vs regional pilots) and devoted a much larger amount of money to incentive payments.

Bundled Payment

We estimated the effects of a hypothetical mandatory Medicare bundled payment program. The main features of the hypothetical program were fashioned after the original design of the Medicare Bundled Payment for Care Improvement Initiative, a voluntary bundled payment program currently being implemented by CMS. (Note: CMS has recently been changing some elements of the design.) Consistent with the Medicare initiative, defined bundles of care include all Medicare Part A and Part B services provided to hospitalized beneficiaries from admission through 30 days post discharge. We created bundles for 10 high-volume, high-cost conditions as defined by 27 Medicare Severity – Diagnosis-Related Groups (MS-DRGs): acute myocardial infarction, congestive heart failure, chronic obstructive pulmonary disease, gastrointestinal bleed, hip fracture, kidney/urinary tract infection, lower extremity joint replacement, pneumonia, septicemia, and stroke. Together, the services included in bundles for these 27 MS-DRGs accounted for 15% of total Medicare Part A and Part B spending in 2008. We set a national base payment rate for each bundle that was adjusted for area-level input prices, similar to the Medicare Inpatient Prospective Payment System. The base payment rate for each bundle was set such that national spending on the bundled services remained unchanged from the baseline (revenue-neutral). In our policy scenario, all providers meeting a minimum volume threshold of 10 bundles per year would receive the bundled payment; providers below the volume threshold would continue to receive payments under status quo policies.

Accountable Care Organizations

We estimated the impact of ACOs on geographic variation in Medicare spending by estimating current ACO enrollees’ locations and assuming a reduction in spending for each enrollee. We identified beneficiaries associated with 148 ACOs participating in Medicare programs as of the end of 2012, plus 77 private sector ACOs that currently participate in various initiatives or pilots. We included private sector ACOs as “proxies” for where future Medicare ACOs might form, allowing us to model a more robust ACO program. In order to define which areas are affected by ACO enrollment, we assigned Medicare beneficiaries in these ACOs to an HRR using data directly from Medicare (when we had such data available). For other ACOs, we assigned beneficiaries to HRRs based on where the primary care physicians in the ACO were located, or if lacking that information, based on the location of associated hospitals or the ACO headquarters. Together, we estimated that the ACOs in our policy scenario would serve roughly 10% of Medicare FFS beneficiaries (7% in Medicare ACOs, 3% in private-sector ACOs). We employed an estimate of per beneficiary savings to Medicare of 3% to 5% depending on the type of ACO—rates somewhat higher than those implied by the literature.9,10 We also assumed proportionally larger spending reductions for ACOs in areas with higher risk-adjusted spending, since these ACOs have potentially greater opportunity for improvement. We made these assumptions in keeping with the objective of simulating the potential impacts on geographic variation of robust versions of the policies in question.


Impact of 3 Policies on Geographic Variation in Spending

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