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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.
The effect of each policy on geographic variation in Medicare spending is illustrated in Figure 1, which separates the 306 HRRs in the United States into quintiles in their initial level of spending (lowest-spending HRRs are to the left) and displays the average change in spending in each quintile, under the 3 policies. Under P4P (the first cluster of columns), it is apparent that the effects on spending are very small and do not show a strong geographic pattern of impacts by spending quintile. The coefficient of variation (CV) of Medicare spending remains at 0.171 both in the baseline and under the policy scenario. Similar patterns emerge when each P4P program is analyzed separately: inpatient, home health, and nursing home (results in eAppendix). The ACO scenario reduces spending in all HRRs, but with a relatively weak geographic pattern. Despite the relatively larger reduction in higher-cost HRRs, the CV under this policy is also unaffected and remains at 0.171. Under the bundled payment scenario, there is a clearer pattern of spending increases in the lower quintiles and spending reductions in the higher quintiles, leading to a reduction in the CV of geographic variation in total Medicare spending from 0.171 to 0.165. The reduction would be just over $400 (or 2.3%) in the highest cost HRR: Miami. In analyses focusing on only the portion of Medicare spending contained within the bundles, the CV under the bundled payment policy is 0.131 compared with a baseline of 0.158.

We conducted a number of sensitivity analyses in which we altered key parameters concerning how the policies were implemented (see eAppendix for results). In 1 alteration, we modeled a version of P4P in which incentive payments were allocated “tournament style” (only the top providers received any payments), and in another, we assumed that ACOs proliferated more widely to include 20% of Medicare FFS beneficiaries. Our results were not sensitive to these alternative scenarios. In alternative scenarios using price-standardized Medicare payment rates (eg, omitting disproportionate share hospital, indirect medical education, and area wage and price adjustments), the reduction in geographic variation was slightly smaller, while alterations removing the volume threshold in the bundled payment program resulted in a slightly larger reduction in variation.

What is Driving the Impact (or lack thereof) of the 3 Policies on Geographic Variation in Medicare Spending?

All 3 of the policies examined would have substantial effects on Medicare payments to individual providers, reallocating payments from low-performing to high-performing providers (the definition of good performance differs among the 3 policies). For example, under bundled payment in the case of acute myocardial infarction, the 5% of providers benefiting the most would receive more than a 20% increase in payments, while the 5% faring the worst would face more than a 15% reduction. Under P4P, 5% of home health providers would receive at least a 15% increase in payments and 5% would face more than an 11% reduction.

Nevertheless, we estimated that 2 of the policies (P4P and ACOs) would have no effect on geographic variation in spending, and that the third (bundled payment) would have a modest effect, with the reason varying across the 3 policies.

The lack of effect of P4P on geographic variation is due to the low correlation between quality and spending in a given area. For example, Table 1 shows little systematic correlation between performance on select inpatient quality measures and inpatient Medicare spending at the HRR level. There is also no consistent relationship between quality and spending for the nursing home and home health quality measures (data not shown).

The bundled payment scenario does exhibit a modest impact on geographic variation in Medicare spending—partly because we assume that Medicare will pay a national base rate (although with geographic adjustments for input price) for bundles of services. This policy would essentially “flatten out” variation in payments per bundle, and those payments represent roughly 15% of Medicare spending in a given year. However, this “flattening” alone would not necessarily reduce geographic variation at the HRR level. For example, imagine that all geographic variation across HRRs was due to variations in outpatient physician visits alone, and that inpatient and post acute care treatment and spending averaged exactly the same in each HRR (though it still would vary among providers inside of each HRR). In that case, the bundled payment scenario would still reduce variation within each HRR but have no impact on geographic variation at the HRR level. This is not the case though. As shown in Figure 2, the same HRRs with high overall spending also tend to have high spending on the bundles of care affected by the policy. Therefore, the policy would result in a reduction in payments to high-spending areas and an increase in payments to low-spending areas, thereby decreasing geographic variation.

There are several reasons the estimated reduction in geographic variation due to bundled payment is modest, however. First, approximately half of the spending on the bundles is related to the facility payment for the hospitalization occurring at the beginning of the bundle. That payment does not change under the bundled payment policy—hospitalizations are currently paid for under the Inpatient Prospective Payment System. Thus, differences in the amount spent on bundles under the new policy are driven by differences in what happens after the inpatient admission—mainly variation in readmission rates and post acute care use by region. Second, only 15% of Medicare spending was captured by the conditions we selected for bundled payment, after excluding providers with a low volume of care for any given bundle from the policy scenario. Lastly, overall spending on bundles in a region is a function of cost per bundle and number of bundles per capita. Bundled payment does not directly address the considerable variation across HRRs in the number of bundles provided. It is possible a bundled payment program could result in changes in the volume of bundles provided, but we lacked a solid evidence base to estimate the direction or magnitude of an expected effect.

Enrollment of Medicare beneficiaries in ACOs would reduce geographic variation in Medicare spending if 2 conditions were met: 1) ACOs do indeed achieve cost savings (assumed in our scenario), and 2) ACOs are more likely to form in higher-cost areas and/or result in larger savings in higher-cost areas. Although we assumed a modest degree of enhanced savings in high-cost areas, the lack of a strong association between area-level ACO formation and Medicare spending ensures the lack of an effect on geographic variation. Figure 3 plots participation in ACOs at the HRR level against HRR-level spending.

Slightly higher ACO penetration exists in higher-cost HRRs but the relationship is weak (r = 0.05; P = .34). As a result, we estimate that ACOs will result in lower Medicare payments to areas with both high and low baseline spending, with little effect on the extent of geographic variation in spending.


Congress is seeking mechanisms to decrease geographic variation in spending, and direct interventions, such as payment rate adjustments to all providers in a region based on spending levels, would certainly be effective in reducing variation. Yet because these may negatively impact low-cost providers in high-spending regions, the IOM Committee on Geographic Variation in Health Care Spending and Promotion of High-Value Care has argued that such changes are an overly blunt instrument. The Committee has argued that provider-focused payment reform policies should be promoted. It is important to understand whether such policies reduce geographic variation in spending.

We estimated the impact on geographic variation in spending of 3 policies (P4P, bundled payment, ACOs) that focus on individual providers, are at the forefront of healthcare payment policy, and could theoretically decrease geographic variation in spending. Each scenario assumed that a sizable fraction of Medicare spending (approximately 10%-15%) would be directly affected by the new payment policy in any given year. The number of beneficiaries affected would be potentially even higher—for example, those not in ACOs may still share physicians with those who are; many beneficiaries are cared for in hospitals or other institutions affected by the bundled payment or P4P policies. The policy scenarios therefore result in substantial reallocations of Medicare payments to providers compared with the status quo. However, we estimated that P4P and ACO scenarios would not change geographic variation at all, and that the bundled payment scenario would only modestly decrease geographic variation—as a point of comparison, the reduction is about half as much as would be achieved from simply removing teaching, outlier, and area wage and input cost adjustments to Medicare payment rates.

As further illustration, the impacts of each of the policies on Medicare spending in selected HRRs from each spending quintile is shown in Table 2. In the high-spending Miami, Florida area, the bundled payment policy reduces spending by roughly $400 per beneficiary (from $18,017 to $17,598) and raises spending slightly in low-spending Rapid City, South Dakota. Impacts on other HRRs and of other policies are generally less than $100 per beneficiary.

It may not be surprising that we found little impact of P4P and ACOs on geographic variation in spending. For P4P to decrease geographic variation in spending, there must be a relationship between spending and quality, and prior work has documented no consistent relationship between these two factors. For ACOs to decrease geographic variation in spending, they must preferentially locate in geographic areas with high spending; prior work has provided mixed results as to whether ACOs are preferentially forming in such regions. Nevertheless, given continued uncertainty, our results emphasize that such policies currently being promoted would be unlikely to reduce geographic variation in Medicare spending.

As the IOM itself has noted, it is unclear whether reducing geographic variation across HRRs is a good metric of successful policy interventions or a national priority. Medical practice is not homogeneous within HRRs, and variation in care between providers instead of regions might prove a better target for policy. Also, measures of geographic variation in total Medicare spending does not account for the important distinction between high-value and low-value spending.

That the policies we investigated had limited impact on geographic variation in spending does not mean they would be ineffective—they were not designed primarily to influence variation. The 3 policies would have substantial effects on Medicare payments to providers, however. As a result, P4P may drive quality improvement; bundled payment and ACOs may improve care and reduce costs. They might also reduce variation in spending among providers within HRRs, but we did not focus on variation at that level. Our results should therefore not be interpreted as evidence that these provider-focused policies are not useful.

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