Medicare: Geographic Areas Used to Adjust Physician Payments for Variation in Practice Costs Should Be Revised
Released: 2007-07-30T00:00:00

Letter

Background

From 1966 through 1991, Medicare paid physicians based on what they charged for services. The Omnibus Budget Reconciliation Act of 1989 (OBRA) required the establishment of a national Medicare physician fee schedule, which was implemented in 1992, replacing the charge-based system. Currently, the Medicare physician fee schedule includes more than 7,000 services together with their corresponding payment rates. In addition, each service on the fee schedule has three relative value units (RVU) that correspond to the three components of physician payment: Physician work—the financial value of physicians’ time, skill, and effort that are associated with providing the service.

Practice expense—the costs incurred by physicians in employing office staff, renting office space, and buying supplies and equipment.

Malpractice expense—the premiums paid by physicians for professional liability insurance.

Each RVU measures the relative costliness of providing a particular service. For example, in 2007, for a mid-level office visit for an established patient, the three RVUs sum to 1.66. In contrast, total RVUs for a chemotherapy infusion procedure are 4.73, indicating that this procedure is almost three times as costly as a mid-level office visit.

Medicare’s geographic adjustment for a particular physician payment locality is determined using three GPCIs that also correspond to the three components of a Medicare payment—physician work, practice expense, and malpractice expense. These GPCIs adjust physician fees for variations in physicians’ costs of providing care in different geographic areas. Other Medicare adjustments to physician fees address issues other than geographic variation in costs. For example, physicians practicing in designated health professional shortage areas receive a 10 percent bonus payment for Medicare services they provide, and physicians practicing in designated physician scarcity areas receive a 5 percent bonus payment for Medicare services they provide.

The GPCIs are numerical factors expressed as the ratio of an area’s cost to the national average cost. For example, in 2007, the practice expense GPCI for Orlando, Florida, is 0.936, which means that the practice expense component of the fee for a service is 6.4 percent below the national average. Because the GPCIs measure physician costs relative to the national average costs, an increase in the GPCIs of one area will result in a decrease in the GPCIs of other areas. In general, GPCIs are higher in urban areas than in rural areas.

To calculate the Medicare payment amount for a service in a particular payment locality, each of the three RVUs for a service is adjusted for geographic differences in resource costs and converted into dollars. This process has several steps. First, to adjust for differences in costs, each of the three RVUs is multiplied by the appropriate GPCI. Second, these adjusted RVUs are added together. Third, that sum is converted into dollars using a conversion factor—a dollar amount CMS calculates that translates each service’s RVUs into a payment amount. The result equals the Medicare payment for that service in that payment locality. For example, to determine the Medicare payment for a mid-level office visit in South Carolina in 2007, first, the three RVUs—work, practice expense, and malpractice expense—are multiplied by the appropriate GPCI (see fig. 1). Second, these adjusted RVUs are summed together to total 1.57. Third, this sum is multiplied by the conversion factor ($37.8975), resulting in a Medicare payment of $59.50 for this service. In the District of Columbia, total adjusted RVUs for a mid-level office visit sum to 1.88, which the conversion factor translates into a payment of $71.25. Physicians practicing in the District of Columbia payment locality receive a higher overall payment for the same service because of the higher costs of operating a private medical practice compared with physicians practicing in the South Carolina payment locality. Since the work, practice expense, and malpractice expense RVUs for a single service are the same in every payment locality, the geographic variation in the Medicare payment for a service mirrors the variation in the GPCIs across payment localities.

CMS is required to review the GPCIs at least every 3 years and, based on that review, may revise them using the most recent data available. The agency last updated the GPCIs in 2005 and is scheduled to review and, if necessary, update them again in 2008. The data used for the different GPCIs are updated on different intervals. Specifically, the decennial census, which is the major data source used in calculating the GPCIs, provides new data once every 10 years. These data are used in calculating the work and practice expense GPCI. HUD data, which are also used in calculating the practice expense GPCI, are updated annually. CMS collects state insurance department and private insurer data, which are used in calculating the malpractice expense GPCI, when the GPCIs are reviewed every 3 years. In addition, CMS is required to review the RVUs at least every 5 years and last updated them in 2007.

GPCIs can be summarized by the GAF, which broadly illustrates differences in costs across physician payment localities. The GAF is an average of the GPCIs, with each of the three GPCIs weighted by the percentage of costs accounted for by its corresponding RVU. Specifically, on average, across all services, work represents 52.5 percent of costs, practice expense represents 43.7 percent, and malpractice expense represents 3.9 percent. For example, to calculate the GAF for the statewide South Carolina payment locality in 2007, the work, practice expense, and malpractice expense GPCIs for South Carolina are weighted and then summed to equal a GAF of 0.931 (see fig. 2). For the District of Columbia payment locality in 2007, the GPCIs are weighted and summed to equal a GAF of 1.133.

Physician Payment Localities Are Primarily Consolidations of the Carrier-Defined Localities That Were Established in 1966, Which CMS Has Since Revised Using Three Approaches That Were Not Uniformly Applied

The current 89 physician payment localities are primarily consolidations of the payment localities that Medicare carriers first defined in 1966. CMS has since revised them over two different time periods using three approaches that were not uniformly applied (see fig. 3). In 1966, Medicare carriers established 240 payment localities, including 16 statewide localities, using their knowledge of local medical practice and economic patterns at the time. These payment localities varied in size, ranging from a single zip code to statewide. For example, California had 28 payment localities, including 8 zip-code-based localities within the county of Los Angeles, whereas New Mexico was a statewide payment locality. According to CMS, the payment locality boundaries were relatively stable for the next 26 years.

In 1989, OBRA required the establishment of a national Medicare physician fee schedule, replacing the charge-based payment system. Under the law, the new fee schedule was phased in over a 4-year period, from 1992 through 1995. To facilitate this transition, CMS continued to use the 240 carrier-defined payment localities, but permitted state medical associations to petition to consolidate their state into one statewide payment locality. Under this approach, from 1992 through 1995, CMS consolidated six states into statewide localities, reducing the number of payment localities to 210, including 22 statewide localities and 28 multiple- locality states.

Consolidation into a statewide payment locality would have generally resulted in urban physicians experiencing a decrease in payment and rural physicians experiencing an increase in payment. Citing this fact, CMS stated it would consider a petition for consolidation from a state medical association that could demonstrate that it had the “overwhelming support” of both groups of physicians. The agency declined to set a numerical level of support that it would consider “overwhelming,” but did enumerate several elements it would require, at a minimum, for state medical associations to demonstrate overwhelming support. CMS assessed the level of physician support by reviewing both the petition from the state medical association and the comments regarding the change that the agency received directly from physicians. For example, in 1995, CMS consolidated Iowa to a statewide payment locality when the state medical association, which represented 75 percent of Iowa physicians, submitted a resolution in favor of consolidation, and 98 percent of the comments CMS received, including 94 percent of comments from physicians who would experience a payment decrease, also supported the transition. CMS has not required medical associations in the states that it consolidated to continue to demonstrate that there is overwhelming support from the physician community for a statewide payment locality.

In 1996, CMS cited a lack of consistency among the carrier-defined payment localities and, in 1997, revised the 28 multiple-locality states. As a result of these revisions, the total number of payment localities was reduced from 210 to the current total of 89. Thirty-four states have statewide payment localities and 16 states have multiple payment localities.

In revising the payment localities in 1997, CMS used two different approaches. Specifically, in 25 of the multiple-locality states, CMS revised the carrier-defined payment localities using a methodology designed to consolidate them. As a result, the agency converted 12 states to statewide payment localities, while it retained multiple payment localities in 13 states. In the remaining 3 multiple-locality states, CMS concluded that its consolidation methodology would have yielded inaccurate localities and therefore created entirely new payment localities. When making these revisions, the agency did not examine any of the 22 then-existing statewide payment localities that had been set using carrier definitions and the overwhelming support policy; therefore, these payment localities have not been examined since they were created, which in most cases was over 40 years ago.

In 25 of the 28 multiple-locality states, CMS applied a methodology that was designed to consolidate the carrier-defined payment localities: new localities could not be created. The agency did not examine the geographic boundaries of the carrier-defined payment localities before consolidating them, even though in 1993, it had stated that the existing payment localities had not been established on “any consistent basis.” Specifically, within the 25 states, CMS ranked the carrier-defined payment localities from highest to lowest cost, as measured by the locality GAF. The agency compared the GAF of the highest-cost payment locality to the weighted average GAF of all lower-cost payment localities in the state. If the percentage difference between the two GAFs exceeded 5 percent, CMS retained the highest-cost payment locality as distinct. It then repeated (or iterated) the process with the second highest-cost payment locality, the third highest-cost payment locality, and so on, until a locality’s GAF no longer exceeded the weighted average GAF of lower-cost payment localities by more than 5 percent. At this point, CMS did not make further comparisons and grouped the remaining payment localities into one Rest- of-State locality. Where the highest-cost payment locality in a state did not exceed the weighted average GAF of all lower-cost payment localities by more than 5 percent, CMS converted the state to a statewide locality.

To illustrate, before the 1997 consolidation, Illinois had 16 carrier-defined payment localities. When CMS applied the consolidation methodology, it found that the GAFs of the 3 highest-cost payment localities (Chicago, Suburban Chicago, and East St. Louis) each exceeded the weighted average GAF of all lower-cost payment localities in Illinois by more than 5 percent, and therefore retained each as a distinct locality. The agency found that the fourth highest-cost payment locality, Springfield, did not exceed the weighted average GAF of all lower-cost payment localities by more than 5 percent; therefore, it consolidated Springfield and the remaining 12 localities into a single Rest-of-Illinois payment locality. In Alabama, CMS found that the GAF of Birmingham, the highest-cost payment locality, did not exceed the weighted average GAF of all lower- cost payment localities by more than 5 percent; therefore, it converted Alabama to a statewide locality.

As part of the 1997 revision, CMS also eliminated all subcounty payment localities, such as those based on zip codes and city boundaries. The agency stated that, in most cases, the 1997 consolidation methodology appropriately consolidated any subcounty payment localities; for example, all payment localities in Arizona, including each of the city-based localities of Flagstaff, Phoenix, Prescott, Tucson, and Yuma, were consolidated into a statewide payment locality. However, in three states—Massachusetts, Missouri, and Pennsylvania—CMS concluded that consolidation of the subcounty payment localities under its methodology would have yielded significant payment inaccuracies. Therefore, in these states, the agency did not apply the consolidation methodology and instead, discarded the carrier-defined payment localities, creating entirely new payment localities based on groupings of counties.

Although CMS cited the payment inaccuracy that would have resulted from the consolidation methodology as the reason for creating new payment localities in these three states, other states had comparably high payment inaccuracy when the methodology was applied. Specifically, CMS determined that the methodology would have yielded the average payment inaccuracies of 3.16, 3.86, and 3.90 percent in Massachusetts, Missouri, and Pennsylvania, respectively. However, it yielded comparable payment inaccuracies when CMS applied it to Kansas and Virginia (3.85 and 3.06 percent, respectively). Despite these comparable payment inaccuracies, CMS did not create entirely new payment localities in Kansas and Virginia because their carrier-defined localities had been county-based and not subcounty-based.

CMS has not revised the geographic boundaries of the physician payment localities since the 1997 revision. Also since that year, CMS has indicated that the only mechanism the agency has set forth to modify the payment localities is for state medical associations to petition for a change by demonstrating that the change has the overwhelming support of the physician community.

More Than Half of the Physician Payment Localities Had Counties within Them with Large Payment Differences

More than half of the physician payment localities we analyzed—47 of 87—had at least one county within them with a large payment difference— that is, there was a payment difference of 5 percent or more between physicians’ costs and Medicare’s geographic adjustment for an area. In total, there were 447 counties with large payment differences, representing 14 percent of all counties. We determined counties with large payment differences by calculating the payment difference between the costs that physicians incur for providing services in a particular county that we calculated (the “county-specific” GAF) compared with Medicare’s geographic adjustment for the locality in which that county is assigned (the “locality” GAF).

Counties with large payment differences were located across the United States and varied in size, whether they were urban or rural, and whether they made up a large or small portion of their locality (see fig. 4). However, a disproportionate number were located in five states. Specifically, 60 percent of counties with large payment differences were located in California, Georgia, Minnesota, Ohio, and Virginia. Of these five states, Minnesota, Ohio, and Virginia are statewide localities for Medicare physician payments.

Large payment differences consisted of both overpayments and underpayments, relative to the county-specific GAFs we calculated. Physicians in 12 percent of counties were overpaid by 5 percent or more, relative to the county-specific GAF. These physicians accounted for 3 percent of Medicare payments to physicians in 2005. In contrast, physicians in 2 percent of counties were underpaid by 5 percent or more, relative to their county-specific GAF, and these physicians accounted for almost 5 percent of Medicare payments to physicians in 2005. This occurs because the volume and costliness of Medicare services delivered by physicians in relatively underpaid counties is much higher than the volume and costliness of services delivered by physicians in relatively overpaid counties. Relative underpayments to physicians may have important consequences for beneficiary access. Officials from several state medical associations told us that geographic areas that are relatively underpaid have difficulty attracting and retaining physicians, which may lead to beneficiary access problems.

Physicians in urban counties, and specifically urban counties within the largest MSAs, had the highest relative underpayment differences, whereas physicians in rural counties generally had the highest relative overpayment differences. Specifically, all counties in which physicians were underpaid by 5 percent or more, relative to their county-specific GAF, were urban (see fig. 5). About three-quarters of these urban counties were part of MSAs with populations of at least 1 million. In contrast, about 60 percent of counties in which physicians were overpaid by 5 percent or more, relative to their county-specific GAF, were rural. More than half of these rural counties had populations of less than 25,000.

Large payment differences occur because many payment localities combine counties with very different costs. Specifically, within 39 of the 87 payment localities we analyzed, county-specific GAFs varied by at least 10 percent. For example, county-specific GAFs in the Poughkeepsie/Northern New York City Suburbs locality ranged from 0.948 to 1.105—a variation of 17 percent.

The fact that many payment localities combine counties with different costs may be due to several factors. First, the current payment localities are primarily consolidations of the localities Medicare carriers established in 1966, and the carriers may have established locality boundaries in 1966 that combined counties with different costs. However, we could not assess the accuracy of the payment localities at the time the carriers established them because no data are available that would allow us to do such an analysis.

Second, a majority of states are statewide payment localities; because such localities contain many counties, they are more likely than nonstatewide localities to combine counties with very different costs. Of the 39 payment localities with county-specific GAFs that varied by at least 10 percent, 23 were statewide. However, several state medical associations we spoke with favor having a statewide payment locality. For example, in Iowa’s statewide payment locality, the highest and lowest county-specific GAFs varied by 11 percent; as a result, 19 percent of payments to physicians in Iowa had a large payment difference. However, an official from Iowa’s state medical association told us that it supports maintaining Iowa’s current statewide payment locality because many physicians in the state maintain urban and rural offices and are not reimbursed for their travel between these offices; having a uniform reimbursement across the state helps mitigate these travel costs.

Large payment differences may also be due to the fact that although large demographic changes have occurred in certain geographic areas, CMS has not revised the payment localities in accordance with these changes. Certain payment localities contain counties that have experienced large population growth relative to the rest of their locality, which may be associated with increasing costs relative to the rest of their locality. For example, physicians in Loudoun County, Virginia, which is part of the Virginia statewide payment locality, were underpaid by 12 percent relative to their county-specific GAF. From 1980 through 2000, the population of Loudoun County increased by 195 percent, while the population of the rest of the Virginia payment locality increased by only 27 percent. Officials from Virginia’s state medical association reported that, because Loudoun County has experienced higher population growth relative to the rest of the state, the area has also become more costly relative to the rest of the state. Accordingly, they stated that physicians from Loudoun County have expressed discontent with Virginia’s statewide payment locality and wish to be reimbursed by Medicare at a rate more representative of their local costs.

Several Alternative Approaches to the Physician Payment Localities Could Improve Payment Accuracy While Generally Imposing Minimal Additional Administrative Burden

Many alternative approaches could be used to revise the geographic boundaries of the current payment localities. We examined five possible approaches and found that three would improve payment accuracy while generally imposing a minimal amount of additional administrative burden on CMS, Medicare carriers, and physicians. Compared to the current payment localities, four of the five alternative approaches would improve payment accuracy, the extent to which each approach accurately measures variations in physicians’ costs of providing care. In addition, while all approaches would impose upfront administrative costs on CMS and Medicare carriers, four of the approaches we examined would impose a minimal amount of additional ongoing administrative burden on CMS, Medicare carriers, and physicians.

Alternative Approaches Could Be Used to Modify the Current Payment Localities

Although many alternative approaches could be used to modify the current physician payment localities, in this report, we present five possible approaches. The approaches and methodologies that we examined are detailed in table 1. Three of our approaches are designed to balance payment accuracy, the extent to which each approach accurately measures variations in physicians’ costs of providing care, with administrative burden. The first of these, the county-based iterative approach, creates a single-county payment locality for each of the highest- cost counties in a state. It then groups that state’s moderate- and low-cost counties together into one “Rest-of-State” locality. In contrast, the second approach, the county-based GAF ranges approach, groups high-, moderate-, and low-cost counties in each state into separate, multiple- county localities. The third approach, the MSA-based iterative approach, creates a single-MSA payment locality for each of the highest-cost MSAs in the nation. It then groups all other counties into a single “Rest-of-Nation” locality. Appendix II contains detailed information on the configuration of the payment localities under each of these approaches, as well as under the current payment localities.

We also present two approaches that are useful for comparison because they illustrate the tradeoffs between payment accuracy and administrative burden. Under the statewide approach, each state has one statewide payment locality. This approach minimizes administrative burden, but maximizes large payment differences. In contrast, under the county-based unique GAF approach, each group of counties in a state with a unique county-specific GAF is a distinct payment locality. This approach minimizes large payment differences, but maximizes administrative burden.

While we limited our analysis to five possible approaches, CMS could examine additional approaches by modifying the ones we selected. For example, three of our approaches use a 5-percent threshold to determine new payment locality boundaries. We used a 5-percent threshold because that is what CMS used for its 1997 consolidation methodology; however, a different percentage threshold may also be feasible. In general, lower thresholds generate more payment localities and further improve payment accuracy. The first time an approach is applied, it is likely to have a large redistributive effect on the payment localities, especially given that many of the localities, particularly the statewide localities, have not been reexamined recently, and in some cases since they were created in 1966. Subsequent changes to the payment localities, if made periodically, would likely be smaller.

Several Alternative Approaches to the Payment Localities Would Improve Payment Accuracy

Compared to the current Medicare physician payment localities, we found that four of our five alternative approaches would improve payment accuracy by reducing the average payment difference between the county- specific GAF and the locality GAF (see fig. 6). For example, compared to the current localities, the county-based GAF ranges approach would reduce the national average payment difference by 52 percent—from 2.3 to 1.1 percent. The statewide approach, however, would increase the average payment difference by 74 percent—from 2.3 to 4.0 percent.

In addition, four of our five approaches would substantially reduce or eliminate relative underpayments to physicians (see fig. 7). For example, under the three county-based approaches, 0 percent of physicians would be underpaid by 5 percent or more, relative to their county-specific GAF. Thus, the number of counties that could potentially experience difficulty attracting and retaining physicians as a result of relative underpayments would also decrease.

Compared to the current localities, the three county-based approaches would also reduce the percentage of payments to physicians who were overpaid by 5 percent or more, relative to their county-specific GAF. However, the statewide and MSA-based iterative approaches would substantially increase relative overpayments. The statewide approach would increase relative overpayments because statewide localities frequently group together counties with very different costs. The MSA- based iterative approach does so because MSAs, which are based on commuting patterns, also frequently group together counties with dissimilar costs. For example, the Atlanta MSA contains 28 counties. The county-specific GAF of the lowest-cost county was 0.821, while the county- specific GAF of the highest-cost county was 1.028. Under the MSA-based approach, however, all counties in the Atlanta MSA would belong to the same payment locality and have the same locality GAF, leading to large payment differences for physicians in certain counties.

Improvements in payment accuracy often lead to increased differences in the GAFs of adjacent payment localities. For example, the county-based unique GAF approach, which minimizes large payment differences, generates the highest average adjacent-locality GAF difference among our alternative approaches (see fig. 8). In general, large differences in adjacent-locality GAFs may be problematic. According to officials from several state medical associations we spoke with, such differences create incentives for physicians to relocate to the higher-GAF payment locality, potentially creating beneficiary access problems in the lower-GAF payment locality. However, the specific instances of high adjacent-locality GAF differences that these officials cited result from payment localities that have large differences between Medicare’s geographic adjustment and physicians’ practice costs. Therefore, in these cases, improvements in payment accuracy actually reduce problematic boundary differences.

For instance, officials from California’s state medical association cited Santa Cruz County, California, as an example, stating that the county is having difficulty recruiting and retaining physicians. This county had a county-specific GAF of 1.119, but is currently part of the Rest-of-California payment locality, which had a GAF of 1.012. Therefore, physicians in Santa Cruz County had a relative underpayment of 10 percent. The adjacent county of Santa Clara has its own, single-county, payment locality, with a GAF of 1.224. Because physicians in Santa Cruz County had such a high relative underpayment, the difference in the locality GAFs between these two counties was very large—21 percent. If physicians in both counties were paid their county-specific GAF, however, the difference between the two county-specific GAFs would be only 5 percent.

We previously reported that income, and therefore GAFs, is only one of several factors that drive physicians’ location decisions. Nonfinancial factors, such as the quality of local schools or a spouse’s employment opportunities, and other financial factors, such as a community’s average income level, are also major influences in physicians’ decisions to locate and remain in certain geographic areas. Accordingly, small increases in the average adjacent-locality GAF difference may not create substantial relocation incentives.

Several Alternative Approaches to the Payment Localities Would Substantially Reduce the Number of Statewide Localities

Four of our five approaches would substantially reduce the number of statewide payment localities (see fig. 9). Statewide payment localities tend to have higher payment differences than nonstatewide payment localities because most states have substantial cost variation among their counties.

Of the 34 current statewide payment localities, all would remain so under the statewide approach. In contrast, all of the current statewide payment localities would become multiple-locality states under the county-based unique GAF approach.

Under the remaining three approaches, the number of states that would remain statewide localities varies. Four current statewide payment localities would remain statewide under all three approaches, 9 would become multiple-locality states under all three approaches, and 21 would remain statewide under some approaches, but not others. The 16 states that currently have multiple localities would generally also have multiple payment localities under the three approaches.

Statewide Payment Localities That Would Remain Statewide under All Three Approaches

The four current statewide payment localities that would remain statewide under each of the county-based iterative, county-based GAF ranges, and MSA-based iterative approaches had relatively low cost variation among their counties. For example, county-specific GAFs in Rhode Island ranged from 1.043 to 1.057, a variation of only 1 percent.

Statewide Payment Localities That Would Become Multiple- Locality States under All Three Approaches

The nine current statewide payment localities that would become multiple-locality states under each of these three approaches had substantial cost variation among their counties. For example, county- specific GAFs in Minnesota ranged from 0.870 to 1.024, a variation of 18 percent. Accordingly, under the county-based iterative approach, Minnesota would have thirteen payment localities; under the county-based GAF ranges approach, it would have three payment localities; and under the MSA-based approach, it would have three payment localities (see fig. 10).

There were 21 current statewide payment localities that would become multiple-locality states under some approaches, but not others. These states generally had more cost variation than states that remained statewide in all three approaches, but less than those that were converted to multiple-locality states in all three approaches. For example, county- specific GAFs in Ohio range from 0.888 to 1.003, a variation of 13 percent. Under the county-based iterative approach, Ohio would remain a statewide payment locality; under the county-based GAF ranges approach, Ohio would have two payment localities; and under the MSA-based iterative approach, it would have five payment localities (see fig. 11).

The 16 states that currently have multiple payment localities would generally also have multiple payment localities under each of the county- based iterative, county-based GAF ranges, and MSA-based iterative approaches. However, depending on the specific state, and approach, the number of payment localities may increase, decrease, or stay the same. This occurs because almost all multiple-locality states had substantial cost variation among their counties. For example, county-specific GAFs in Florida ranged from 0.910 to 1.073, a variation of 18 percent. Florida currently has three payment localities. Under the county-based iterative approach, the state would have five payment localities; under the county- based GAF ranges approach, it would have three payment localities; and under the MSA-based iterative approach, it would have nine payment localities (see fig. 12).

Several Alternative Approaches to the Payment Localities Would Generally Impose a Minimal Amount of Additional Administrative Burden on CMS, Medicare Carriers, and Physicians

Four of our approaches would generally impose a minimal amount of additional administrative burden on CMS, Medicare carriers, and physicians. This occurs because these four approaches would generally create three or fewer additional localities in each state. In total, these four approaches create from 36 fewer to 132 more payment localities than currently exist (see fig. 13). For example, the county-based iterative approach would generate 132 additional localities, for a total of 219. The statewide approach would generate 36 fewer localities, for a total of 51. The county-based unique GAF approach, however, would generate 1,054 additional localities, for a total of 1,141—over 13 times the current number.

The number of localities generated by the county- and MSA-based iterative approaches, however, could be reduced with very little loss in payment accuracy by regrouping single-county and single-MSA payment localities with similar GAFs, respectively, into larger payment localities. For example, by combining localities with county-specific GAFs that vary by 1 percent or less, the total number of payment localities under the county- based iterative approach could be reduced from 219 to 139, while only increasing the average payment difference from 1.5 to 1.6 percent. For example, in Kansas, under the county-based iterative approach, Wyandotte County, which has a county-specific GAF of 0.972, and Johnson County, which has a county-specific GAF of 0.975, would both become distinct single-county payment localities. However, under a regrouping methodology, these counties could be regrouped into a two-county payment locality while increasing the average payment differences of these counties from 0 percent to about one-tenth of 1 percent.

CMS officials we spoke with stated they would experience onetime upfront costs if the current payment localities were modified, regardless of the number of localities generated by the approach chosen. Specifically, CMS creates a distinct physician fee schedule for each payment locality and would have to perform data reliability checks on the localities’ physician fee schedules to ensure their accuracy. Agency officials stated that they would have to reprogram CMS systems, update its files that assign carriers and physicians to a payment locality, and provide physicians with extensive education on the payment locality modifications. However, CMS officials stated that they did not anticipate that significant modifications to the payment localities would require a substantial amount of additional ongoing administrative burden.

In addition, CMS officials stated that any change to the payment localities would cause Medicare carriers to incur upfront costs. Representatives from the five Medicare carriers that we spoke with each stated that a moderate increase in the number of payment localities would not require a substantial amount of additional resources. They each indicated that modifying the payment localities would cause onetime transitional costs. Specifically, they would be required to create new data files that assigned each physician to a new payment locality. Carrier representatives also indicated that an increase in the number of payment localities would increase their ongoing operational costs. Specifically, the carriers must load each of the distinct physician fee schedules CMS sends them into their data systems and then perform data reliability checks on them to ensure they are accurate.

Physicians would not incur additional administrative burden if their payment locality changed. In addition, physicians in California we spoke with stated that if the current localities were modified, they would not experience an increase in administrative burden and would complete the same paperwork as they do currently. CMS officials we spoke with agreed that physicians’ paperwork requirements would remain the same. In addition, representatives from the Medicare carriers we spoke with stated that they do not anticipate having to provide physicians with significant additional training about payment locality modifications, since most carriers already routinely send each physician a complete fee schedule specific to their payment locality.

Modifying the payment localities will cause physicians’ locality GAFs to change, and accordingly, physicians will have to transition to new reimbursement rates. Representatives from the American Medical Association we spoke with expressed concern that transitioning to new reimbursement rates could be burdensome to physicians. However, we found that under four of our five approaches, locality GAFs would neither increase nor decrease substantially, relative to current locality GAFs (see fig. 14). For example, under the county-based GAF ranges approach, locality GAFs for one-half of 1 percent of Medicare physician payments would experience a decrease of 5 percent or more, while locality GAFs for about 4 percent of payments would experience an increase of 5 percent or more. Under the statewide approach, however, locality GAFs for about 15 percent of Medicare physician payments would experience a decrease of 5 percent or more, while locality GAFs for about 10 percent of payments would experience an increase of 5 percent or more. Rural counties would generally account for most of the counties with a decrease of 5 percent or more in Medicare’s geographic adjustment.

Conclusions

Adjusting Medicare payments for the costs physicians incur in operating a private medical practice in different parts of the country is important to ensure that Medicare accurately accounts for variations in physicians’ costs of providing care, and that beneficiaries have sufficient access to physician care. However, more than half of the current physician payment localities had counties within them with large payment differences—that is, there was a payment difference of 5 percent or more between physicians’ costs and Medicare’s geographic adjustment for an area. In addition, CMS’s lack of a uniform approach to revising payment localities has resulted in localities where there is substantial cost variation, a particular problem among the 34 statewide localities. We have identified three alternative approaches to the current payment localities that, if uniformly applied to all states, could be used to improve payment accuracy while generally imposing a minimal amount of additional administrative burden. This is consistent with the goal that CMS has stated in setting the geographic boundaries of payment localities.

While, under four of our five alterative approaches, payments to physicians would not change substantially overall, rural counties would generally account for most of the counties with a large decrease in Medicare’s geographic adjustment. However, CMS has other payment policies specifically designed to ensure that physicians practicing in rural areas, such as those designated as physician scarcity areas, are able to recruit and retain physicians, helping ensure beneficiary access. Other approaches are possible as well and CMS could phase in implementation over several years, for example, to lessen the effect on physician payments in areas negatively affected by changes to the current physician payment localities. Using an approach that would be uniformly applied to all states would likely have a large redistributive effect on the payment localities the first time the approach was applied, especially given that many of the localities, particularly the statewide localities, have not been reexamined recently, and in some cases since they were created in 1966. Subsequent changes to the payment localities, if made periodically, would likely be smaller.

Currently, CMS has no mechanism in place to periodically update the physician payment localities to ensure that the geographic boundaries of the payment localities accurately address variations in the costs of operating a private medical practice. Other components of the physician fee schedule are routinely reviewed—the RVUs every 5 years, and the GPCIs every 3 years. Updating the geographic boundaries of physician payment localities at least every 10 years when new decennial census data become available—the major data source used in the calculation of the GPCIs—would ensure that Medicare appropriately accounted for changes in the geographic distribution of physicians’ costs of operating a private medical practice.

Recommendations for Executive Action

To help ensure that Medicare’s payments to physicians more accurately reflect geographic differences in physicians’ costs of operating a private medical practice, we recommend the following two actions. First, we recommend that the Administrator of CMS examine and revise the physician payment localities using an approach that is uniformly applied to all states and based on the most current data. Second, the Administrator should examine and, if necessary, update the physician payment localities on a periodic basis with no more than 10 years between updates.

Agency Comments and Our Evaluation

CMS reviewed a draft of this report and provided comments, which appear in appendix III. CMS stated that it appreciated the work we had done in examining this issue and that our analysis would serve as a helpful resource as it continues to examine payment locality alternatives.

CMS stated it would consider our first recommendation—to examine and revise the physician payment localities using an approach that is uniformly applied to all states and based on the most current data. The agency also stated that, in doing so, it would give full consideration to the redistributive effects and administrative burdens of any change to the payment locality structure. We agree that redistributive effects and administrative burden should be considered when making the necessary changes to the physician payment localities.

Regarding our second recommendation—that CMS examine and, if necessary, update the payment localities on a periodic basis—the agency stated that it considers payment locality issues when concerns are raised by interested parties and based on its own initiative, an approach that it believes is more flexible and efficient than examining the payment localities every 10 years. Reviewing payment localities in response to concerns raised by interested parties, however, could result in CMS examining only selected physician payment localities, rather than examining all payment localities using a uniform approach. Updating the payment localities at least every 10 years when new decennial census data become available would ensure that Medicare appropriately accounts for changes in the geographic distribution of physicians’ costs of operating a private medical practice.

CMS also stated several concerns about specific points in the report. The agency asserted that our use of counties as the basis for comparing physician costs and Medicare’s geographic adjustment implies that county- level data are measured with absolute precision but the data we used to calculate county-specific physician costs are proxies for actual costs. We recognize that the data we used to calculate county-specific physician costs are proxy measures. As noted in the draft report, we calculated our measure of physician costs using the same data sources and methodology CMS uses to calculate the GPCIs, which are the agency’s proxy measures of physicians’ costs. In 1991, the year before the GPCI’s implementation, CMS noted that the cost would be prohibitive to collect the detailed locality-level data needed to measure every area’s staff costs and other expenses compared to the national average. The agency therefore limited data sources to those that existed and were readily available, selecting data proxies for each GPCI. As the agency uses the GPCIs to adjust physician fees for variations in physicians’ costs of providing care in different geographic areas, we determined that this measure was sufficient for our purposes. CMS also asserted that the data we used to calculate county-specific physician costs are proxies because, for more than 90 percent of counties, the Census Bureau data we obtained were based on data for larger geographic areas. As noted in the draft report, although Census Bureau data were not available at the county level for all counties, we were able to obtain county-specific data for 1,091 of the 3,142 counties in the United States—about 35 percent. Also as noted in the draft report, these 1,091 counties represented 83 percent of the U.S. population in 2000, and 88 percent of Medicare’s payments to physicians in 2005. We have, however, clarified in our report that the data we used to calculate physician costs are proxy measures.

CMS commented that the draft report’s characterization of payments to 14 percent of counties as “inaccurate” was highly inappropriate and potentially problematic. The agency stated that it was concerned that a finding that payments were inaccurate could be construed to mean that there has been an overpayment for which recoupment of the overpayment, as well as other actions, should be pursued. As a result, we have deleted the term and instead define counties with a payment difference of 5 percent or more as having a “large payment difference.” As we did in the draft report, however, we use the term “payment accuracy” to refer to the extent to which the payment localities accurately measure variations in physicians’ costs of providing care in different geographic areas.

CMS expressed a concern that our report did not sufficiently account for the effect our recommended changes would have on physicians. Specifically, the agency stated that increasing payments to physicians in some counties in a state would reduce payments to physicians in other counties in a state, and that our report did not sufficiently convey the extent to which our alternative approaches would reduce physician payments in certain areas. As noted throughout the draft report, because GPCIs measure physician costs relative to the national average costs, an increase in the GPCIs of one area will result in a decrease in the GPCIs of other areas. With the exception of the MSA-based iterative approach, each of our alternative approaches examines physicians’ costs within a state and was therefore in accordance with the principal of within-state “budget neutrality,” which provides that adjusting Medicare payments should neither increase nor decrease the total amount of Medicare payments to physicians. We recognize that the potential for large payment reductions is an important issue and have added information to the report to address it.

CMS commented on our finding that several alternative approaches to the payment localities would generally impose a minimal amount of additional administrative burden. Specifically, the agency stated that it believes the level of administrative burden would be more significant than what we presented in our draft report. We believe that our report accurately portrays the level of administrative burden that CMS would incur if the payment localities were modified. In the draft report, we stated that the agency would experience onetime upfront costs if the current payment localities were modified, regardless of the number of localities generated, but that they did not anticipate that significant modifications to the payment localities would require a substantial amount of additional ongoing administrative burden. In addition, using an approach that is uniformly applied to all states would likely have a large redistributive effect on the payment localities the first time the approach was applied, especially given that many of the localities have not been reexamined recently, but if subsequent changes were made periodically, they would likely be smaller. However, we have modified the report to include additional information on the types of upfront costs CMS would incur if the payment localities were changed.

CMS also stated that our draft report did not point out the potential implications an increased number of payment localities would have on physicians’ administrative burden. Specifically, the agency stated that increasing the number of payment localities also increases the likelihood that physicians will practice in multiple localities and therefore have to file claims based on multiple localities. However, physicians are already required to include the address of the facility where services were rendered on the claim. As noted in the draft report, physicians we spoke with stated they would not incur additional administrative burden and would complete the same paperwork as they currently do if the payment localities were modified; CMS officials we spoke with concurred with this statement.

CMS commented on our description of the agency’s denial of California’s state medical association’s 2004 proposal for a change to the payment localities. Specifically, CMS stated that it does not believe that its denial of the California proposal demonstrates reluctance on the part of the agency to consider and adopt changes to the payment localities. We did not state in the draft report that the agency’s denial of the California proposal demonstrated a reluctance to consider and adopt changes to the payment localities. Rather, we stated that, since 1997, CMS has indicated that only one state medical association has petitioned for a change to the payment localities—California’s state medical association. CMS denied its petition, stating that the agency did not have the statutory authority to make the specific change the association had requested.

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Appendix I: Scope and Methodology

In conducting this study, we analyzed data obtained from the Census Bureau, the Department of Housing and Urban Development (HUD), and the Centers for Medicare & Medicaid Services (CMS). We interviewed officials from CMS and representatives from five Medicare Part B carriers that process physician claims in 27 states. We also interviewed representatives from the American Medical Association and the state medical associations from California, Colorado, Florida, Iowa, Minnesota, New York, North Carolina, Ohio, Texas, Virginia, and Washington. These states represent geographically diverse areas, as well as Medicare physician payment localities that were established in 1966 using carrier definitions, localities that were revised from 1992 through 1995 using a physician overwhelming support approach for a statewide locality, and localities that were revised in 1997 using a CMS approach designed to consolidate carrier-defined localities. In addition, we interviewed county medical associations and 11 physicians from San Diego, Santa Cruz, and Sonoma Counties in California, and Albany County, New York, which were referred to us by representatives from the state medical associations we spoke with.

To determine how CMS has revised the physician payment localities since they were established and the approaches the agency used, we reviewed relevant documents published in the Federal Register to determine when and how the boundaries of the localities have changed, and a CMS- contracted report on the payment localities that was used as the basis for the agency’s 1997 modifications. To determine the extent to which the current payment localities reflect the costs of providing care in different geographic areas, we used the geographic adjustment factor (GAF). The GAF is a weighted average of the three geographic practice cost indices (GPCI)—work, practice expense, and malpractice expense. We constructed a proxy measure of the costs physicians incur for providing services in a particular county (the county-specific GAF) and compared this measure with Medicare’s geographic adjustment for the locality to which that county is assigned and is a proxy for physicians’ costs in a locality (the locality GAF). We compared the two by calculating the “payment difference,” the absolute value of the county’s 2005 locality GAF minus its county-specific GAF, divided by its county-specific GAF.

To calculate county-specific GAFs, we calculated GPCIs using the same methodology CMS used for the most recent GPCI update, in 2005. Specifically, we computed county-level work and practice expense GPCIs using 2000 Census Bureau data on the median earnings of six categories of nonphysician professional occupations, fiscal year 2006 HUD data on fair market rents, and 2005 CMS data on county-level relative value units (RVU)—a measure of the relative costliness of providing a particular service. These data were the most recent data available at the time of our analysis. Although we refer to these data and GPCIs as “county-specific,” we were not able to compute unique county GAFs for each of the 3,142 counties in the United States because Census Bureau data are not available at this level. Specifically, it is Census Bureau protocol to suppress statistics for which less than three people report values and, in certain cases, nonmetropolitan counties had less than three persons reporting earnings for a profession. Therefore, we were able to obtain data that allowed us to calculate individual work and practice expense GPCIs for the 1,091 counties that were part of a metropolitan statistical area (MSA) and one composite work and one composite practice expense GPCI for each non-MSA area per state. In 2000, counties in MSAs represented 83 percent of the population, and in 2005, they represented 88 percent of Medicare’s payments to physicians. We used the Office of Management and Budget’s MSA definitions as of December 2005.

The data CMS uses to calculate the malpractice expense GPCIs are not available at the county level. However, the malpractice expense GPCI is weighted by only 3.9 percent when calculating the GAF. Thus, to calculate the county-specific GAFs, we computed the weighted average of the county-level work and practice expense GPCIs and the locality-level malpractice expense GPCI. In addition, we defined a county as urban if it was part of an MSA and as rural if it was not part of an MSA. Our analysis was limited to the 87 payment localities within the 50 states and the District of Columbia.

We assessed the reliability of the CMS, Census Bureau, and HUD data in several ways. First, we performed tests of data elements. For example, we examined the Census Bureau data on the median earnings of certain professions to determine whether these data were complete. Second, we reviewed existing information about the data elements. For example, we compared the county-level work and practice expense GPCIs we calculated to less-recent county-level work and practice expense GPCIs provided by CMS. Third, we interviewed a CMS official and a Census Bureau official knowledgeable about the data and reviewed documentation related to the data. We determined that the data used in our analyses were sufficiently reliable for our purposes.

To evaluate whether alternative approaches to the Medicare payment localities could improve payment accuracy without imposing a substantial amount of additional administrative burden, we used the county-specific GAFs to construct five different payment locality configurations. We evaluated the payment accuracy of each approach, the extent to which each approach accurately measures variations in physicians’ costs of providing care, based on its payment difference, that is, the absolute value of the county’s 2005 locality GAF minus its county-specific GAF, divided by its county-specific GAF. Because improvements in payment accuracy may increase the differences in the GAFs of adjacent payment localities, which could potentially create beneficiary access problems, we examined the differences between the GAFs of adjacent payment localities. We calculated adjacent-locality GAF differences as the absolute value of the difference in locality GAFs between all unique, contiguous, county pairs. We weighted the average adjacent-locality GAF difference by the sum of the RVUs of the contiguous counties. We evaluated the administrative burden of each approach based on the number of payment localities that it generated as well as interviews with CMS officials, Medicare carrier representatives, and physicians.

Although many alternatives exist, in this report we present five possible approaches for constructing the payment localities. Three of our approaches are designed to balance payment accuracy with administrative burden. We also present two approaches that are useful for comparison because they illustrate the tradeoffs between payment accuracy and administrative burden.

Of the three approaches that balance payment accuracy with administrative burden, two are based on counties, the smallest geographic unit for which GAFs can be constructed from the data sources available, and one is based on MSAs. There are two important general distinctions between our two county-based approaches and our MSA-based approach. First, under the county-based approaches, it is possible for adjacent counties in an MSA to belong to different payment localities. In addition, as CMS has done in the past, our county-based approaches create payment localities within a state: no payment locality crosses state lines. In contrast, under our MSA-based approach, in order to keep MSAs intact, all the counties in an MSA belong to the same payment locality and wherever an MSA crosses state lines, its payment locality crosses state lines as well.

Our three approaches that balance payment accuracy with administrative burden use two distinct methodologies: the iterative methodology and the range methodology. The iterative methodology creates single-county or single-MSA payment localities for the highest-cost areas and “Rest-of” localities for the remaining areas. Specifically, the county-based approach creates one payment locality for the moderate- and low-cost counties in each state, which we refer to as the “Rest-of-State” payment localities. The MSA-based approach creates a single payment locality that combines moderate-cost MSAs, low-cost MSAs, and non-MSA areas from many different states, which we refer to as the “Rest-of-Nation” payment locality. The range methodology creates a payment locality for each group of similar-cost counties within a state. Generally, under this methodology, moderate- and low-cost counties within a state are assigned to different payment localities. For each of these approaches, we used a 5-percent threshold because that is what CMS used for its 1997 consolidation methodology. However, a different percentage threshold may also be feasible.

Of the two approaches that illustrate the tradeoffs between payment accuracy and administrative burden, under the statewide approach, each state has one statewide payment locality. This approach minimizes administrative burden, but maximizes large payment differences. In contrast, under the county-based unique GAF approach, each group of counties in a state with a unique county-specific GAF is a distinct payment locality. This approach minimizes large payment differences, but maximizes administrative burden.

We conducted our work from June 2006 through May 2007 in accordance with generally accepted government auditing standards.

Appendix II: Information on Configuration of the Current Medicare Physician Payment Localities and the Alternative Approaches

Appendix II: Information on Configuration of the Current Medicare Physician Payment Localities and the Alternative Approaches City, Arlington, Fairfax, Fairfax City, Falls Church City in Virginia; Montgomery, Prince George’s in Maryland 2 Broward, Collier, Indian River, Lee, Martin, Palm Beach, St. Lucie 1 Butts, Cherokee, Clayton, Cobb, DeKalb, Douglas, Fayette, Forsyth, Fulton, Gwinnett, Henry, Newton, Paulding, Rockdale, Walton 2.26 Average payment difference in percentage factor (GAF)

2 DuPage, Kane, Lake, Will 3 Bond, Calhoun, Clinton, Jersey, Macoupin, Madison, Monroe, Montgomery, Randolph, St. Clair, Washington 1 Jefferson, Orleans, Plaquemines, 1 Anne Arundel, Baltimore, Baltimore City, Carroll, Harford, Howard 2 Rest of Maryland, except Montgomery and Prince George’s counties 1 Middlesex, Norfolk, Suffolk 1 Macomb, Oakland, Washtenaw, 1 Clay, Jackson, Platte 2 Jefferson, St. Charles, St. Louis, 3.06 Average payment difference in percentage factor (GAF)

1 Bergen, Essex, Hudson, Hunterdon, Middlesex, Morris, Passaic, Somerset, Sussex, Union, Warren 2 Rest of New Jersey 2 Bronx, Kings, Nassau, Richmond, Rockland, Suffolk, Westchester 4 Columbia, Delaware, Dutchess, Greene, Orange, Putnam, Sullivan, Ulster 5 Rest of New York 1 Clackamas, Multnomah, 1 Bucks, Chester, Delaware, Montgomery, Philadelphia 2.69 Average payment difference in percentage factor (GAF)

1 Statewide, except Alexandria City, Arlington, Fairfax, Fairfax City, Falls Church City The locality GAF is Medicare’s 2005 locality GAF without the work GPCI floor or Alaska adjustments.

Average payment difference in percentage 50 Minneapolis–St. Paul–Bloomington, MN– 82 Milwaukee–Waukesha–West Allis, WI represents counties that were grouped into the “Rest-of-Nation” locality.

Appendix III: Comments from the Centers for Medicare & Medicaid Services

Appendix IV: GAO Contact and Staff Acknowledgments

Acknowledgments

In addition to the contact named above, Thomas A. Walke, Assistant Director; Margaret S. Colby; Jennifer DeYoung; and Joanna L. Hiatt made major contributions to this report.