Medicare adjusts payments to hospitals according to their geographic location in designated labor markets. As discussed in Chapter 2, a labor market is a market in which employers compete for a common group of workers, such as nurses, and workers compete for a common set of jobs, such as those in the health care industry.
For reasons explained in Chapter 2, it is difficult to definitively establish the boundaries of labor markets. Metropolitan statistical areas (MSAs), a core-based statistical area (CBSA) designed under the auspices of the Office of Management and Budget (OMB), have generally been accepted as reasonable approximations of local labor markets because they are defined on the basis of core population centers surrounded by counties that have high levels of economic integration with that core (see Chapter 2 for additional discussion of MSAs).
Regardless of what method is used to define labor markets, boundary issues will arise. Some neighboring providers who know that they compete for the same labor will find themselves classified into different wage areas and subject to different geographic adjustment factors. If the wage index values are very different on either side of labor market borders (what are sometimes called “wage cliffs”), this leads to a perception that the index is inaccurate or unfair. Smoothing the labor market boundaries is a way of addressing these border issues by reducing the index differences between nearby areas. Incorporating a smoothing adjustment into a geographic price adjuster is a way of acknowledging that fixed market boundaries cannot always accurately represent economic activity.
In Chapter 2, the committee recommended that the same labor market definitions should be used for both the hospital wage index (HWI) and the physician geographic practice cost indexes (GPCIs). In the committee’s view, both the HWI and the GPCIs should reflect geographic variation in input prices rather than variation in cost, because costs are determined by both price and production decisions such as choice of types of labor. For this reason the committee has recommended the use of a fixed weight index—that is, one that captures geographic variation for a fixed set of occupations in fixed amounts. The committee also recommended in Chapter 2 that MSAs and statewide non-MSAs be used to define input markets for both hospitals and physicians.
This chapter builds on the description of labor markets in Chapter 2 and the discussion of the HWI in Chapter 3. It begins by providing background on the current wage index, the extent of the “wage cliff” problem, and how wage differentials between nearby areas are addressed under the current geographic adjustment system through the system of reclassifications and exceptions described in Chapters 1 and 3. The chapter goes on to describe other approaches for refining markets using formula-based smoothing techniques, and then examines modeling results for three specific smoothing methods that were evaluated by the committee. Finally, the chapter offers the committee’s recommendations for a smoothing approach based on commuting patterns of health care workers.
The original current HWI is computed from Inpatient Prospective Payment System (IPPS) hospital data, after adjusting the hourly wages for occupational mix differences but before making any labor market reassignments or other adjustments. The values of the 2011 original (or “pre-reclassification”) index range from 0.671 to 1.638, excluding values in the territories. Reclassification and other adjustments narrow the range—the lowest index value after reclassification is 0.743. Reclassification also reduces the number of wage cliffs. Under the original index, there are 1,709 pairs of hospitals located within 25 miles of each other that have an index value difference of 0.10 points or more. Under the final post-reclassification index, there are only 614 pairs of hospitals with a difference of 0.10 points or more.
Adjustments and exceptions to labor markets under the current IPPS can be grouped into three types: those with a rationale based on commuting patterns, those with a rationale based on individual hospital wages, and those that serve a policy or political objective but are not based on technical improvements to the index (see Table 4-1).
“Lugar counties” were the first wage index exceptions, enacted as part of the Omnibus Reconciliation Act of 1987. Lugar counties are nonmetropolitan counties located at the edges of non-MSA rest-of-state labor markets, where there is documentation that a substantial part of the population commutes into the neighboring MSA.1 Hospitals located in a Lugar county are “deemed urban” and automatically re-reclassified into the neighboring MSA.2
The most common type of labor market adjustment is reclassification granted through the Medicare Geographic Classification Review Board (MGCRB). Most of these are individual hospital reclassifications, and they can be granted for hospitals wanting to reclassify from a nonmetropolitan market to a nearby MSA, or from one MSA to another MSA. As described in Chapter 3, criteria for individual hospital reclassifications are based on geographic proximity to higher-wage markets as well as hospital-specific wage costs. Hospitals must meet what are known as “wage comparability criteria,” which require that the hospitals’ own average hourly wage is both comparable to the average wage of the labor market to which they are requesting reclassification and higher than the market wage where they are geographically located.
MGCRB can also grant requests to re-designate whole nonmetropolitan counties as metropolitan, which will qualify all hospitals in the county for reclassification into a neighboring
1 Code of Federal Regulations, Section 412.63.
2 The Omnibus Reconciliation Act of 1987 (P.L. 100-203) simply “deemed” them to be part of the neighboring MSA; a later amendment in the Omnibus Reconciliation Act of 1989 (P.L. 1-1-239) revised this and established Lugar counties as a type of reclassification in order to avoid penalizing the rural markets in which Lugar hospitals were physically located.
|Adjustment||Based on Commuting Patterns||Based on Hospital Wages||Based on Other Characteristics|
|Lugar Counties (n = 55)||X|
|MGCRB Reclassificationsa (n = 810)|
|Whole County||X||X (group)|
|Individual Hospital||X (individual)|
|Section 505 “Outmigration” Adjustments (n = 215)||X|
|“Rural Floor” (n = 184)||X|
|“Frontier State Floor” (n = 49)||X|
a MGCRB = Medicare Geographic Classification Review Board. Section 401 providers are included in the MGCRB reclassifications category.
MSA. County re-designations are based on a combination of criteria that include both wage comparability and commuting patterns. Specifically, recent commuting data must demonstrate levels of economic integration similar to those that the OMB (2000) uses to identify outlying counties in the CBSA metropolitan area definitions.3
Section 505 of the Medicare Modernization Act of 2003 (MMA) introduced a new type of wage index adjustment that is based primarily on commuting patterns and is available to hospitals that are not reclassified by the MGCRB (CMS, 2004).4 Known as the “outmigration adjustment,” it provides for wage index changes for qualifying hospitals located in qualifying counties where at least 10 percent of resident hospital workers are commuting to hospitals located in other MSAs with a higher wage index. The adjusted index is a weighted average of the wage index for the home (or resident) county and the indexes for the work area counties.
The underlying assumption behind the Section 505 adjustments is that if workers in a given county are able to commute to a neighboring labor market with higher wages, then the prevailing wages faced by the given county’s hospitals will be higher than prevailing wages faced by other hospitals in their labor market. Many hospitals facing this situation will pay higher wages and will meet the wage comparability criteria for wage index reclassification. Eligibility for Section 505 adjustments, however, is limited to hospitals that are not reclassified. They must be located in counties that meet the 10 percent outmigration threshold, and they must have an average county hospital wage that is higher than the average hospital wage of their assigned labor market.
The last two adjustments on Table 4-1 are those for “rural floors” and “frontier floors.” These adjustments have the effect of reducing variation in payments across areas, but they are unrelated to border issues or market misclassification.
What is called the “rural floor” adjustment was enacted by the Balanced Budget Act of 1997. It is actually an index floor for urban hospitals because it establishes that MSA index values within each state cannot be any lower than the state’s nonmetropolitan index value.5
3 The Medicare Geographic Classification Review Board, Section 412.63.
4 Section 505 of P.L. 108-173.
5 Section 4410 of P.L. 105-33. In addition, a related category of “imputed rural floors” was created through a special temporary regulatory measure (CMS, 2004) that provided related relief for states with no hospitals located in rural counties. Rules enabling imputed rural floors are set to expire in FY 2012.
Hospitals in MSAs that are subject to a rural floor are not reclassified—they are simply paid using the higher index of the nonmetropolitan area, rather than their own markets’ computed index. No proximity or wage comparability requirements are associated with the rural floors. RTI identified 46 MSAs across 20 states that were subject to rural floors in FY 2011, resulting in a higher wage index for 215 IPPS hospitals.
Frontier state wage floors are the most recent type of wage index policy adjustment, having been enacted as part of the Patient Protection and Affordable Care Act of 2010 (ACA). These set a lower limit of 1.00 on index values for any labor markets located within five states that have very low population densities (referred to as “frontier states”), regardless of the actual level of relative wages. The five states are Montana, Nevada, North Dakota, South Dakota, and Wyoming. Nonmetropolitan markets in all five states and metropolitan markets in 10 of their metropolitan areas benefit from this floor. The frontier state floors result in a higher wage index for a total of 49 hospitals.
The committee sought to recommend a method of addressing labor market border problems that will reduce the need for reclassifications and exceptions. The committee believes that application of a consistent and data-driven smoothing process applied to MSA-based markets can help to reduce the number and magnitude of wage cliffs while remaining faithful to the basic wage index principles described in Chapter 1.
The committee studied the problem of wage cliffs under MSA-based labor markets and reviewed three formula-based smoothing techniques in more detail: the first is contiguous-county smoothing, similar to what was proposed in 2007 by the Medicare Payment Advisory Commission (MedPAC); the second is commuting pattern–based smoothing similar to what is currently used for the outmigration adjustment; and the third is geospatial smoothing as implemented through geographic information systems software. These approaches were each evaluated as possible alternatives to the current set of administrative adjustments now used to address wage index boundary problems.
The committee believes that any proposed approach to smoothing should:
- be plausibly linked to the notion of improved accuracy in labor market definitions;
- use computations that are transparent and reproducible by the provider community;
- rely on data that are publicly available, reliably produced, and periodically updated;
- be systematic and formula-based, to minimize the need for individual reviews or exceptions processes;
- be made budget-neutral in implementation;
- focus on markets rather than individual facilities; and
- avoid use of hospital-specific criteria or costs.
The last two objectives are grounded in both conceptual and practical concerns. Provisions to adjust the wage index should focus on areas where there is evidence that a local market is misrepresented by MSA and statewide non-MSA definitions, rather than evidence that a given
facility is disadvantaged. A given facility’s labor costs are a reflection of both the prevailing wages in its area and the decisions that the facility makes about the types of labor to hire. In keeping with the principles identified in Chapter 1, the underlying goal of a price index should be to adjust only for the first of these.
From a purely practical perspective, it is also important to recognize that a change from the Centers for Medicare and Medicaid Services (CMS) wage data to wage data from the Bureau of Labor Statistics (BLS) as a source for the index will mean that the computation of average hourly wages from the Medicare cost report Worksheet S-3 will no longer be needed. The source for “comparable wage data,” which is used to defend arguments for hospital-specific adjustments, may therefore no longer be available. This provides another reason to develop an adjustment that avoids reliance on individual hospital labor costs.
In June 2007, MedPAC recommended using BLS/Occupational Employment Statistics (OES) hourly wage data in the HWI (MedPAC, 2007). At that time, the Commission also suggested a contiguous-county smoothing algorithm that could be applied to the BLS index values to reduce large border differences as an alternative to hospital-level geographic reclassification. The rationale underlying contiguous-county smoothing is that large differences in index values between communities located on either side of an MSA boundary may be the result of distortion due to MSA-level averaging rather than true local variation in the price of labor.
To implement this method, MedPAC analysts first set a maximum tolerable difference in wage index values across any given border. Although the choice of a specific threshold may be arbitrary, the threshold can be modified easily in modeling to allow policy makers and regulators a chance to see how sensitive the smoothed index is to the choice. MedPAC chose a threshold of a 10 percent positive difference in the wage indexes of two contiguous counties, such that the smoothing algorithm would be applied only where the wage index applicable to a particular county was less than or equal to 90 percent of the wage index of a contiguous county. All pairs of counties along any side of all MSA and non-MSA borders were evaluated to see if the difference in their wage indexes exceeded the tolerance level. If the threshold difference was exceeded, then the wage index applicable to the county with the lower index was adjusted up to the 90 percent threshold.
Smoothing can be designed so that adjustments are made only for the county with the lower index value (i.e., positive values only), or so that adjustments are made for both counties (i.e., positive and negative values). By allowing smoothing only in counties where the wage index was 90 percent or less of the wage index in a contiguous county, MedPAC chose to model smoothing so that it would produce only positive adjustments. A positive-only adjustment algorithm will raise the national aggregate wage index. Therefore, MedPAC also applied a budget neutrality adjustment to the wage index values of all providers, to offset the payment effect of the positive contiguous-county adjustments (see Box 4-1 for discussion of budget neutrality adjustments).
Both the original wage index and the geographic practice cost indexes (GPCIs) are to affect the geographic distribution of Medicare payments while having no net impact on the total amount being distributed. This is because they are cross-sectional indexes, measuring variation across geographic units at a single point in time, and centered on a value of 1.00 that represents the national average value of the item being indexed. An individual market is either below the national average (index <1.00), equal to the national average (index = 1), or above the national average (index >1.00). By construction, a weighted (or aggregate) average of all the individual market index values should always be 1.00. Whenever an administrative change is made to any of the original individual index values, however, the weighted average of the altered index will change, becoming greater than or less than 1.00 according to the net effect of the adjustments. Administrative changes are nearly always made for purposes of increasing index values. Consequently, the weighted average altered index is always pushed above 1.00. Using an altered index to adjust payments will therefore alter not only the distribution of the payments but also the total amount being distributed.
Most of the exceptions and adjustments that are made to the wage index are required by statute to be “budget neutral,” meaning they cannot alter the total amount of payment being distributed. The only way to accomplish this is to impose an additional computation, made after the various exceptions and adjustments are completed, that brings the aggregate average of the altered index values back to 1.00. This final computation is implemented as an across-the-board adjustment imposed on all providers. Thus, the net positive effect of any set of special exceptions can be thought of as a corresponding reduction imposed across all providers.
The Centers for Medicare and Medicaid Services (CMS) is asked to make many changes and adjustments to many components of the prospective payment system in each year’s rule-making, and almost all of these must be made budget-neutral. Some budget neutrality adjustments are made by adjusting the base payment rates applicable to all providers, but wage index neutrality can be enforced by across-the-board offsets to the wage index applicable to all providers. Wage index neutrality adjustments have recently been in the range of 1.0 to 1.5 percent, and these are made primarily to accommodate the effects of reclassification and rural floors. (Congress did not require the implementation of frontier state floors and outmigration adjustment to be budget neutral.)
The various smoothing simulations performed for the committee also included estimations of budget neutrality factors specific to each simulation. The neutrality factors for the commuter based smoothing algorithms, for example, were estimated by calculating the worker-weighted average of the post-smoothed index; if this number was greater than one, then the net impact of the smoothing was to raise the aggregate wage index, and if it was less than one, then the net impact of the smoothing was to lower the aggregate wage index.
In either instance, to bring the values back to levels with an aggregate average of one, each market’s index value was divided by the worker-weighted average of the post-smoothed index.
RTI’s contiguous county smoothing model used data for 3,413 Inpatient Prospective Payment System (IPPS) hospitals located across 1,595 counties. Three versions were run based on threshold differences where the target county index had to be 85 percent, 90 percent, or 95 percent of the adjacent county index. All IPPS hospitals within a metropolitan statistical area (MSA) or rest-of-state market were assigned the hospital wage index value from the pre-reclassified Centers for Medicare and Medicaid Services (CMS) wage index for that area. For each target county with an index value that was less than the threshold percent below that of a contiguous county, the index for all hospitals in the target county was raised to the value where the threshold was met. If there were more than one contiguous county meeting the threshold, the index value of the target county hospitals was raised to the value where the threshold was met for the contiguous county with the highest index. Following the Medicare Payment Advisory Commission’s model, only positive adjustments were made. This created a need for offsetting index neutrality adjustments ranging from –0.3 percent, when an 85 percent standard was used, to –3.6 percent, when a 95 percent standard was used (see Box 4-3 for a further description of budget neutrality adjustments).
It is possible for one round of adjustments to create new index differences above the tolerance level in a new set of contiguous counties. We used an iterative approach where counties are reassessed after each computed adjustment to identify possible new wage cliffs, and the computation is repeated until there are no more cliffs. The 85 percent model needed only two iterations, the 90 percent model needed four, and the 95 percent model needed nine.
As part of its deliberations, the committee modeled the impact of contiguous-county smoothing using a simplified version of MedPAC’s approach.6 The RTI simulations used the CMS pre-reclassified index for FY 2011 as a base rather than an alternative BLS index, in order to compare the impact of smoothing to the impact of the current adjustments (see Box 4-2).
Table 4-2 identifies the number of counties and hospitals affected. Using an 85 percent threshold (the index of the lower-wage county is no greater than 85 percent of the higher-wage county), 9 percent of counties and 7 percent of hospitals qualify for an adjustment. The offsetting reduction in all index values needed to fund the positive-only smoothing adjustments is –0.3 percent. Using a 95 percent threshold, two-thirds of counties and 58 percent of hospitals qualify for a smoothing adjustment. The results suggest that a 90 percent threshold could be reasonable from a policy perspective.
6 The MedPAC algorithm had two stages: the first was referred to as blending and the second as smoothing. The blending stage recognized within-state variation, using available county-level data from the 2000 census for four key health care occupations. For each county the index value before smoothing was the MSA-level BLS index value, adjusted to incorporate one half of computed within-MSA county variation as documented from the 2000 census data. The blended portion of the adjustment could have a negative or positive effect on a county index level, but the smoothing portion of the adjustment was implemented only for positive changes.
Because RTI modeled contiguous-county smoothing using the CMS wage index, the analogous computation for the first stage of the MedPAC algorithm would have been to compute the county average wage from the CMS wage index files and use this for the blending step. This was not done here, in part because the committee’s objective is to explore ways to address border issues that do not rely on individual hospital data.
|Threshold for a County to Qualify for an Adjustmenta||Percent of Counties Affectedb||Percent of IPPSa Hospitals Affected||Index-Neutrality Adjustment Needed to Offset Effect of Positive Adjustmentc|
a Data represent a percentage of the neighbor county index.
b Computed as percentage of counties that have at least one IPPS hospital.
c Computed as the percent reduction in wage index values for all hospitals that is needed to bring the national aggregate index value back to 1.00.
Table 4-3 shows the distribution of wage index adjustments created by contiguous-county smoothing using a 90 percent threshold. Only 6 percent would see moderate increases (5–10 percent) as compared to the pre-reclassified index, and only 4 percent would see increases greater than 10 percent.
A similar proportion would see increases greater than 5 percent as compared to the final (post-reclassification) index. These are most likely hospitals that receive no special adjustments under the current system. In RTI’s simulations the wage index values for 62 percent of hospitals would be slightly higher under a contiguous-county smoothing algorithm than under the current system. However, this is not because they are being given upward adjustments under the smoothing algorithm. Rather, it is because the offsetting budget neutrality factor is smaller under the smoothing algorithm than it is under the current reclassification system.
A contiguous-county smoothed index would be lower than the current final index for roughly 30 percent of IPPS hospitals. Some of these hospitals, however, are currently benefiting under the rural or frontier floors. Ideally, results from smoothing should be compared to results from reclassifications and other adjustments exclusive of the rural and frontier floors, because these are designed to accomplish something other than improve the technical accuracy of the wage index. Unfortunately, it is difficult to separate the effects of floors from other adjustments because some hospitals currently benefiting from the rural floor would likely apply for and receive geographic reclassification if not for those floors.
Contiguous-county smoothing is a fairly transparent approach to market smoothing. It is reasonably intuitive and can be easily reproduced by the provider community. A drawback to the approach is that it is based on a proxy measure rather than a direct measure of economic activity. It builds on the premise that geographic proximity is sufficiently correlated with economic integration so that adjacent areas can be assumed to face similar prevailing wages.
Labor markets are also influenced by factors such as topography, transportation, demographics, and location of commercial centers. An analysis of the contiguous-county smoothed index values against the actual hospital hourly wages revealed that slightly more hospitals
|Impact on Index Value||Comparison of Smoothed Index With:|
|Original FY 2011
Hospital Wage Index
|Final Wage Index
(Post-reclassification and All Other Adjustments)
|IPPS Facilities||IPPS Facilities|
|Decrease of more than 10%||0||0||94||2.8|
|Decrease of 10% to 5%||0||0||370||10.8|
|Decrease of 5% to 0%a||2,797||81.9||562||16.5|
|Increase of 0% to 5%||283||8.3||2,102||61.6|
|Increase of 5% to 10%||206||6.0||197||5.8|
|Increase of more than 10%||127||3.7||88||2.6|
a Decreases as compared to the original pre-reclassified wage index are limited to those from a budget-neutrality adjustment of negative 1.2 percent.
would meet the current wage comparability criteria for reclassification using smoothed index values than meet them using unsmoothed values. If hospitals’ actual average hourly wages are considered indicative of a market, then these results would not provide strong support for the notion that contiguity necessarily implies shared labor markets.
Commuting Pattern–Based Smoothing
The committee turned to commuting patterns as a possible basis for refining the MSA-based payment areas because commuting is a more direct measure of economic integration. While proximity is a strong determinant of economic activity, commuting patterns will reflect the combined influence of proximity, topography, transportation, demographics, and commercial activity.
An example of a single highly integrated area where employers should be facing the same prevailing wages would be two contiguous counties with large proportions of residents commuting to or from both counties. The degree of commuting in and out of defined communities is a useful measure to capture this set of circumstances.
Commuting patterns are already incorporated into the designation of CBSAs. Metropolitan and micropolitan areas are defined by identifying a core population nucleus, and linking the core with adjacent communities having a high degree of “economic and social integration” with that core. “Economic and social integration” measured exclusively using commuting data from census surveys.7 Outlying CBSA counties are assigned to a central CBSA core county if
(a) at least 25 percent of the workers in that outlying county commute to work in one of the core counties, or (b) at least 25 percent of the jobs in the outlying county are filled by residents of one of the core counties (OMB, 2000). Assignment is based entirely on commuting and not by population size or population density. Thus, a relatively non-urbanized county with low population density can still be considered part of a metropolitan labor market area if a sizable portion of its population is employed in the core. Both the designation as a Lugar county and eligibility for whole-county re-designation by MGCRB are based on evidence that the 25 percent criteria have been met.
County commuting patterns also serve as the basis for the Section 505 outmigration adjustment, as discussed earlier in this chapter and in Chapter 1. Where CBSAs are based on commuting patterns of all workers, the outmigration adjustment is based on the specific commuting patterns of hospital workers. This is an important distinction, because hospitals are not located in every county; consequently, hospital commuting patterns for smaller communities can look very different from commuting patterns of other workers. The data are from a special tabulation of Census 2000 journey-to-work data, compiled from responses to the decennial census “long-form” survey. As discussed in the final IPPS payment rules for FY 2005, the data were collected from the one-in-six households that received the long form, and the tabulations used by CMS were restricted to responses from individuals coded as working in the industry code 622000 that includes all hospitals (CMS, 2004). CMS described several limitations of the data in its proposed and then final rules for that year, including small cell sizes and uncertainty about future availability, but it received no public comments strongly opposing the data and no recommendations for alternative sources. CMS did not rule out the possibility of collecting commuting data directly from hospitals at some time in the future.
Although the long-form sample was not repeated for the 2010 census, journey-to-work data are now collected by the census as part of the American Community Survey (ACS), which uses smaller samples but fields the surveys over multiple years.8 The 5-year ACS journey-to-work data (surveys from 2006–2010) is expected to be released in 2012, with special tabulations by respondent characteristics available for request by 2013.9
Commuting pattern–based smoothing can be implemented on the basis of the patterns of workers residing elsewhere but commuting to the county where a provider is located (in-commuting), or on patterns of residents leaving a county to work in another county where a provider is located (out-commuting). Both measures capture economic integration to some degree, and most counties where a hospital is located have workers going in both directions.
The balance of commuting, however, is from lower-wage areas to higher-wage areas. This is because workers tend to seek higher wages and because larger hospitals are located in larger, higher-wage metropolitan areas. Smoothing based on out-commuting will tend to raise the wage index in areas where a hospital is competing for workers with facilities located in higher-wage markets. Conversely, smoothing based on in-commuting patterns will tend to lower the wage index in areas where hospitals are drawing large pools of workers from lower-wage markets. Out-commuting adjustments would therefore raise the aggregate national wage index, while in-commuting adjustments would lower it. In either implementation, a budget neutrality adjustment is needed that offsets the aggregate effect (see Box 4-1 for a further explanation).
8 See http://www.census.gov/acs/www/. The ACS replaced the U.S. Census long-form survey in 2010. Conducted by the Bureau of the Census, the ACS is a nationwide continuous survey that collects additional demographic, housing, and economic data in the years between decennial census. ACS as a source for wage data is also discussed in chapter 5.
9 Personal communication from journey-to-work section chief, U.S. Census Bureau (May 18, 2011).
The committee first examined cross-county and cross-MSA commuting patterns from the special census tabulation used by CMS, and then it created simulations for a number of commuting pattern–based smoothing adjustments that resemble expanded forms of the current outmigration adjustment.
The 2000 census data set identified 1,596 counties as having a hospital and 2,730 counties as having any hospital workers. Roughly 40 percent of those 2,730 counties therefore “exported” all of their hospital workers to other counties. Figure 4-1 illustrates the distribution of in- and out-commuting across counties. In counties with at least one hospital, the median percentage of workers coming from another county was 21 percent.
Commuting across different MSAs or statewide nonmetropolitan areas is less common than is commuting across counties within MSAs. One-third of the counties with hospital workers had no cross-MSA commuting. Among those counties with any, the median percent of workers commuting out-of-MSA was 19 percent.
It is possible to simulate the impact of commuting pattern–based smoothing using any given index and tabulations of all combinations of counties by worker residence and worker employment. Box 4-3 provides a description of the computations needed to implement commuting pattern–based smoothing.
Sample computations and illustrative maps are also presented in Table 4-4 and Figure 4-2. These illustrate commuter pattern–based smoothing for two hypothetical counties that are located in a moderately well-integrated area that crosses several MSA boundaries. In this example, workers are commuting in both directions between County A and County D even though County D has more hospital workers and a higher wage index (1.100 compared to 0.950).
If adjustments are made based on patterns of workers that commute out of their resident county (out-commuting), and if both positive and negative adjustments are made, the resulting adjustments for this area would be an increase in County A’s index from 0.950 to 0.998 and a drop in County B’s index from 1.10 to 1.066. In this example, smoothing succeeds in reducing the wage cliff from a difference of 0.15 points to a difference of 0.07 points.
The committee reviewed simulations that estimated the impact of commuting pattern–based smoothing on both the CMS wage index and a new index constructed from the May 2010 release of BLS Occupational Employment Statistics (OES) data.10 Models were run using the out-commuting percentages, consistent with the approach used by CMS for its Section 505 out-
10 All models using BLS data are based on hospital wage indexes constructed for the committee by BLS staff in order to incorporate published and non-published data. The wages used were from surveys of all health care sector employers, and fixed weights for the index were drawn from the BLS employment estimates for short-term hospitals using the 31 standard occupation codes that were recommended by MedPAC (2007).
migration adjustment. The models used out-commuting patterns for 1,585 counties matched to the location of 3,468 hospitals identified in the FY 2011 IPPS Impact File.11
Several design issues need to be decided in order to implement this approach to smoothing. Many of them are similar to the issues addressed by CMS when the Section 505 outmigration adjustments were introduced:
11 One county within the Los Angeles area had no commuting data from 2000, although five hospitals were located in this county in 2011.
To model commuting pattern–based county smoothing, RTI used the same special census tabulation file that is used by the Centers for Medicare and Medicaid Services (CMS) for outmigration adjustments. The file contains data for each combination of county of worker residence (“home county”) and county of hospital employment (“work county”), identifying the number of hospital workers qualifying for both.
Each county where a hospital is located is a potential target for commuting pattern–based adjustment. For each target county, RTI computed the number of resident workers who commuted out of the county for a job in a hospital, and identified the wage index applicable to each of the counties to which resident workers were commuting. An adjusted wage index for the target county is computed as the worker-weighted average of the wage index values for each county where its resident hospital workers are employed. However, if workers commute to counties located within the same labor market as the county in which they reside (“within–metropolitan statistical area [MSA] commuting”), then their “home counties” and “work counties” have the same wage index and commuting patterns have no effect on the wage index of the target county.
To limit smoothing to counties with substantial out-commuting, the adjusted index can be computed only for counties where a minimum threshold of workers commute out of the county or out of the MSA. Alternatively, the weighted average computation can be modified such that the wage index and commuting levels of any one destination work county is used in the formula only if the commuting to that one county exceeds a specific threshold.
Similarly, to limit smoothing to positive adjustments only, the adjusted index can be implemented only for counties where the wage index is increased. Alternatively, the weighted average computation can be modified such that the wage index of any one destination work county is used in the formula only if it is higher than the wage index applied to the target county.
- CMS chose to implement outmigration adjustments with a 10 percent commuting threshold. The advantages of a threshold are that it minimizes disruption and administrative costs from having many small adjustments within a market. A threshold would also focus the wage index adjustments on areas where there is clear evidence of cross-MSA integration. The downside of a threshold, however, is that it creates another administrative “cliff.” A weighted average computation without thresholds would result in adjustments that are directly proportional to the level of commuting.
- CMS chose to implement the outmigration adjustment without regard to the size of the index differences. An index-difference threshold would focus the smoothing adjustments on areas with true “wage cliffs.” As with the commuting threshold, the disadvantage of setting an index-difference threshold is that it creates another administrative barrier with a potential to be perceived as arbitrary. In addition, the current wage index is often recomputed during the year in response to data errors or new legislation, and linking eligibility for a smoothing adjustment to current index levels could add instability to the process. Using the BLS index, this would be less of a concern.
- By statute, the current outmigration adjustment is limited to positive adjustments. This issue is related to the question of whether budget neutrality should be imposed locally or
|No. of County Resident Hospital Workers||Work in County||County Located in MSA||Percentage of Total Resident Workers||Original Index||Computation of Smoothed Index as Weighted Average|
|Out of County A|
|700||A||1||54||0.9500||0.54 × 0.9500 =||0.5115|
|50||B||1||4||0.9500||0.04 × 0.9500 =||0.0365|
|100||C||2||8||0.9000||0.08 × 0.9000 =||0.0692|
|450||D||3||35||1.1000||0.35 × 1.1000 =||0.3808|
|Out of Adjacent County D|
|1,200||D||1||71||1.1000||0.71 × 1.1000 =||0.7765|
|100||A||1||6||0.9500||0.06 × 0.9500 =||0.0559|
|150||C||3||9||0.9000||0.09 × 0.9000 =||0.0794|
|250||E||2||15||1.0500||0.15 × 1.0500 =||0.1544|
nationally (see Box 4-1). If both positive and negative smoothing adjustments are made, then the resulting payment redistributions are localized and most of the index increases will be offset by index decreases within the same set of areas. If adjustments are only made to areas where out-commuting is to a higher-wage area (thus raising the index value for the county from which the workers are commuting), then the aggregate effect of all of the increases must be offset by a national index or budget neutrality adjustment that spreads the cost of the smoothing-based increases across all areas and all providers. If the adjustment is intended only for IPPS providers (as is the case with the outmigration adjustment), then hospital commuting data are the appropriate measure. If journey-to-work survey sample size were not an issue, the adjustments could be tailored to fit the commuting patterns of hospitals for a hospital index, skilled nursing facilities (SNFs) for a SNF index, or ambulatory care workers for physician offices, because all of these industry codes are available from the survey data. In practice, sample size limitations may dictate that commuting patterns for all health care workers be used. This would make the method generalizable to other provider settings.
The committee discussed each of these implementation options for commuter pattern–based smoothing. Multiple simulations were run to test the sensitivity of resulting adjustments to these design parameters.
Results from three outmigration models are presented here.
- The first model adjusts index values for all counties where hospital workers living in that county commute to another labor market with a different wage index—whether the index is higher or lower.
- The second model limits adjustments to counties where at least 10 percent of hospital workers commute to labor markets with a different wage index.
- The third model limits the adjustments to counties where at least 10 percent of hospital workers commute to labor markets where the wage index is higher than the index of the home county. This ensures that only positive smoothing adjustments are made (at least until the imposition of the budget neutrality factor). This third specification is similar to what CMS now uses for the outmigration adjustments.
Each of the three models was run once using the FY 2011 CMS pre-reclassified wage index, and once using an index computed for the committee by staff at the BLS, using OES data from their May 2010 release of data collected between 2007 and 2009 (see footnote 12 in this chapter). Adjustments were computed using the same special tabulation of 2000 census hospital worker commuting data that CMS has been using because it is the best publicly available source for health care commuting patterns at this time.
In the simulations run with CMS index values, 64 percent of the counties with IPPS hospitals had at least some resident hospital workers commuting to another MSA or non-MSA rest-of-state market and, therefore, could be affected by smoothing based on out-commuting (see Table 4-5). Applying a 10 percent minimum commuting threshold for eligibility reduces this
|Smoothing Design Parameters|
|Smoothed CMS Index
|Smoothed BLS Index|
|Countiesa||IPPS Hospitals||Countiesa||IPPS Hospitals|
|All counties eligible, no minimum commuting thresholds, both positive and negative changes implemented||64.4||63.9||62.1||61.1|
|Counties eligible only if .10 percent of workers commute to another labor market||35.8||32.1||35.2||31.6|
|Counties eligible only if .10 percent of workers commute to another labor market that has a higher wage index||26.8||22.0||25.7||23.0|
a Computed as a percentage of counties that have at least one IPPS hospital.
number to 36 percent. Further restricting the model to positive-only index adjustments reduces it to 27 percent. Simulations run on the BLS index data produce very similar results.
Tables 4-6 and 4-7 provide additional detail on results from the CMS and BLS index models, respectively. In the CMS data model with no restrictions on county eligibility, 27 percent of metropolitan counties and 54 percent of nonmetropolitan counties qualify for an increase in their indexes. In comparison, 40 percent of metropolitan counties and 9 percent of nonmetropolitan counties experience a decrease. The size of the adjustments ranges from a reduction of 4.6 percent to an increase of 19.3 percent, but these are outlier values. Most changes are very small in absolute terms. Imposing the 10 percent outmigration threshold reduces the proportion of counties with a negative adjustment to 12 percent in metropolitan areas and 6 percent in the non-MSA rest-of-state areas. These negative adjustments would not be implemented in the model with the positive-only adjustments, but the other adjustments would remain the same. Estimates of budget neutrality factors to fund adjustments under the different specifications are similar, ranging from a nationally applied reduction of 0.27 percent to a nationally applied reduction of 0.38 percent
The effects of commuter pattern–based smoothing on the BLS wage index are similar but smaller than the effects on the CMS wage index. Budget neutrality adjustments range from a decrease of 4.2 percent to an increase of 16.4 percent, and the differences by metropolitan and nonmetropolitan counties follow the same pattern.
Figure 4-2 shows how the simulated adjustments are distributed across hospitals. The figure shows the average percent change in index values for groups of hospitals categorized according to the type of wage index adjustment they have in FY 2011. Bars show the results for each of the three models, for each hospital group. The upper frame of Figure 4-2 shows results from the CMS index models, and the lower frame shows results from the BLS models.
|Optional Smoothing Parameters|
|Percent change in index value (excluding effect of budget neutrality factor)|
a Includes only counties that have at least one Inpatient Prospective Payment System (IPPS) hospital.
This presentation approach highlights several findings:
- First, design parameter choices such as threshold versus no threshold, or positive and negative adjustments versus positive only, have relatively little effect on the overall impact of smoothing across the hospital groups.
- Second, the impact of smoothing on BLS index values is smaller than the impact on CMS index values, but the relative effect across hospital groups are almost identical (see also Table 4-8). This reflects the strong correlation between the two indexes (the Pearson coefficient is 0.90). It also suggests that BLS data are able to capture hospital market differences as well as hospital-only data.
- Third, as might be expected, the types of hospitals that would benefit most from commuting pattern–based smoothing are those that are already receiving adjustments based on commuting patterns (specifically, the Lugar county and outmigration adjustments). Commuting pattern–based adjustments applicable to hospitals currently receiving reclassification, however, are much smaller than are their reclassification adjustments.
|Optional Smoothing Parameters|
|Percent change in index value (excluding effect of budget neutrality factor)|
a Includes only counties that have at least one Inpatient Prospective Payment System (IPPS) hospital.
Figure 4-3 presents only the unweighted average effect across hospitals in each group. Table 4-8 provides additional detail on the distribution of the commuting pattern–based adjustments by comparing the change in CMS and BLS index values when outmigration smoothing is used.
Other Geospatial Approaches
The committee also discussed several techniques for defining or refining markets that are based on distances between hospitals. The geospatial approaches described in this section can be used to construct completely new markets from local wage data, or they can be adapted to adjust the boundaries of previously defined markets if wage data have already been aggregated (as is the case with BLS data).
One geospatial approach is similar to the “nearest neighbor” concept that was developed by the Prospective Payment Advisory Commission (predecessor to MedPAC) and recommended to the Health Care Financing Administration (HCFA) in 1987. Each hospital is designated as a cen-
|Number of Hospitalsa||Smoothed Versus Unsmoothed CMS Index||Smoothed Versus Unsmoothed BLS Index|
|Min||25th pct||50th pct||75th pct||Max||Min||25th pct||50th pct||75th pct||Max|
|Large urban areab||1,384||–12.79||–0.87||0.22||0.90||17.45||–9.48||3.13||6.47||10.48||23.41|
|Other urban areab||1,120||–26.68||–2.97||–1.05||0.48||26.30||–15.89||1.55||5.70||10.68||25.13|
|By Current Wage Index Statusc|
|Section 505 (outmigration)||246||–14.06||–1.72||–0.64||0.12||3.59||–15.00||7.41||11.31||14.46||21.02|
|Frontier state floor||49||–31.87||–15.01||–13.52||–5.04||5.14||–13.70||–10.40||–6.60||–5.60||13.40|
|MSA w/ rural floor||215||–23.78||–7.68||–2.92||–1.51||5.45||–10.44||0.00||3.29||8.44||25.13|
a Number of IPPS hospitals identified in the FY 2011 IPPS Impact File, excluding five that were located in counties without hospital worker commuting data.
b Large urban area is CMS’s designation for an MSA with population ≥1 million. Other urban area is an MSA with less than 1 million population. Rural area refers to all non-MSA counties.
c Wage index status derived from FY 2011 Impact File. Hospitals counted in wage index floor areas are only those whose wage index is affected by the floor.
tral point around which a circle is drawn based on distance or time (for example, a 60-mile radius, or a 1-hour commuting radius). Each hospital defines its own market, such that the approach produces multiple overlapping markets rather than a set of mutually exclusive markets with fixed borders. Within each hospital-specific market, a weighted average wage can be constructed from the hourly wage data for hospitals within that radius. The approach could also be implemented using average wages computed for very small geopolitical units (such as census tracts or zip codes) within the radius, if the data were available. An adaptation of this approach might simply average the previously computed MSA-based wage index values that fall within the radius.
The nearest-neighbor approach can also be adapted to use commuter data rather than physical geography, by substituting the notion of “commuter sheds” for fixed distances. Instead of drawing a fixed radius around the target hospital, the commuter shed approach would define the relevant local markets based on the counties that contribute workers to a hospital, or a hospital county. The local weighted average wage can be constructed for each hospital or county based on the average wages of the counties contributing to that county’s workforce.
More complex approaches to smoothing that use individual hospital location and distance functions are available by applying methods developed through geographic information systems (GIS). A commonly used algorithm for grouping data by location is based on an inverse distance weighting (IDW) function. IDW is a method of interpolation that adjusts a data point for a given location by averaging the sample data points in the neighborhood of the target value. The closer a point is to the center of the data point to be adjusted, the more influence (or weight) it has in the averaging process.
IDW smoothing applied to the wage index would identify a central geographic point within a market, such as the city center of an MSA or a population “centroid,” and adjust the wage index values of surrounding hospitals based on how closely they are located to that central point. Hospitals that are located at the edge of their labor market and relatively far away from its central point could have their index values affected only marginally by that central point. Hospitals located at the edge of their labor market, but close to the centroid of a neighboring market, could have their wage index affected primarily by the neighboring centroid. IDW approaches for a wage index can be implemented using the actual average wage at the central points and allowing index values to be adjusted up or down based on the “pull” of the central point as measured by distance. It is also possible to implement this technique using only the area wage index values for the central point and all individual hospital adjusted points; the second implementation would capture varying levels of influence across labor markets based on location within markets, but it would not capture the influence of the central point within the market.
The committee reviewed simulation results from the second IDW implementation just described, using the existing FY 2011 pre-reclassified wage index as its base. Straight-line distances were computed from geo-coded hospital street addresses. Results were generated using a standard application of ARC-GIS software where the weighting function was the inverse of the squared distance.
As expected, the model sharply reduced the number of nearby hospitals with wage index differences of 0.10 or more, compared with the number as computed from the original pre-reclassified index and also compared with the number as computed from the post-index (see Table 4-9).
Table 4-10 shows the distribution of wage index adjustments created by this application. For reference, the first column shows the impact of administrative reclassifications and other adjustments on the final wage index compared to the pre-reclassification index. The second column
|Distance Between Hospitals (miles)||Number of IPPS Hospital Pairs with Wage Index Differences of 0.10 Points or More|
|Under Pre-reclassification Wage Index||Under Final Post-reclassification Wage Index||Under Pre-reclassification Wage Index with IDW Smoothing|
shows the impact of IDW smoothing compared to pre-reclassified wage index values, and the third shows the impact of IDW smoothing compared to the hospitals’ final wage index values.
Under IDW smoothing, wage index values are increased by 1 percent or more for about one-third of hospitals in the model, and they are decreased by 1 percent or more for 29 percent of hospitals in the model. Under the current system of reclassifications and adjustments, the wage index is increased by 1 percent or more for about 29 percent of hospitals, while very few hospitals have decreased index values beyond the effect of the budget neutrality adjustment.12
Because the IDW approach computes both positive and negative adjustments it is largely self-weighting, and should not require an offsetting national index neutrality adjustment. Put another way, IDW smoothing is “locally neutral.”
IDW smoothing based on the existing wage index values is successful in reducing wage cliffs and should therefore also reduce perceptions of boundary issues among providers. After reviewing the results from the simulation of geospatial methods, however, the committee feels that the approach also has several drawbacks.
First, IDW models are highly technical and require specialized software. Because the computations are iterative and complex, the methods could be difficult for the provider community to replicate, which is contrary to the committee’s objectives of promoting transparency.
Second, the approach smoothes the boundary differences by reducing large wage cliffs and offsetting them with many new smaller differences among local area providers. The approach can only be implemented as a “locally budget neutral” method where an increase in one provider’s index is offset by other relatively local decreases.
Finally, and most importantly, IDW is driven solely by considerations of distance from a
12 A small number of hospitals in the FY 2011 Impact File show reclassified wage index values that are lower than pre-reclassified values, and a small number of hospitals located in Texas appear to be misclassified as rural. No values in the Impact File were altered for this study, but there are some unexpected results (such as those showing a small number of facilities with large decreases in the wage index following reclassification).
|Impact on Index Value||CMS Final Indexb||IDW-Smoothed
Index Compared with Pre-reclassified Index
Index Compared to Final (Post-reclassified) Indexb
|Decrease of more than 10%||4||0.1%||31||0.9%||113||3.3%|
|Decrease of 10% to 5%||12||0.4%||220||6.5%||449||13.2%|
|Decrease of 5% to 1%||13||0.4%||735||21.7%||983||29.0%|
|Change from –1 to +1 %||2,353||69.4%||1,273||37.5%||1,124||33.1%|
|Increase of 1% to 5%||431||12.7%||448||13.2%||349||10.3%|
|Increase of 5% to 10%||279||8.2%||440||13.0%||259||7.6%|
|Increase of more than 10%||299||8.8%||244||7.2%||114||3.4%|
a Number of IPPS hospitals identified in the FY 2011 IPPS Impact File and included in all three indexes. IDW simulations were conducted with data for facilities in the 48 contiguous states only.
b This is the post-reclassified index compared with the pre-reclassified index, including effects of reclassifications, “deemed” metropolitan counties, outmigration adjustments, rural floors, and frontier floors.
chosen point, on the premise that proximity is a good proxy for economic integration. The committee recognizes that labor markets are highly influenced by location, but topography, transportation, and demographics also play a significant role in defining market behavior. If commuting patterns are available, then commuting data can provide a direct measure rather than a proxy.
Review and Implications
The committee reviewed several options for refining the definitions of labor markets by smoothing their borders. Each option, including the current set of administrative changes, has advantages and disadvantages. Table 4-11 is presented as an aide to reviewing CMS’s current approaches as well as the alternatives just presented, to assess them systematically in the context of the objectives set out at the beginning of this chapter.
Commuting pattern–based smoothing meets all of the objectives identified for smoothing at the start of this chapter. It offers several advantages over the other approaches:
- It is solidly linked to notions of markets and what defines a market;
- It is based on data that can capture changes in labor markets;
- It is flexible in implementation design;
- It is reasonably transparent in computation; and
- It has a precedent in the current prospective payment system.
After reviewing the findings using different design parameters for commuting pattern–based smoothing, the committee concluded that several of the decisions on design parameters would
|Committee’s Stated Evaluation Objectives||Current Administrative Approaches||Alternative Approaches|
|Hospital Reclassification||County Re-designation||Out-migration Adjustment||Index Floors||GIS: Inverse Distance Weighting||Contiguous-County Smoothing||Commuting Pattern–Based Smoothing|
|Is plausibly linked to the notion of improved accuracy in labor market definitions||Yes||Yes||Yes||No||Possibly||Possibly||Yes|
|Uses computations that are transparent and reproducible by the provider community||Yes||Yes||Yes||Yes||No||Yes||Yes|
|Relies on data that are publicly available, reliably produced, and periodically updated||Yes||Yes||Yes||Yes||Yes||Yes||Yes|
|Is systematic and formula-based, to minimize the need for individual reviews or exceptions processes||No||Lugar counties only||Yes||Yes||Yes||Yes||Yes|
|Is made budget-neutral in implementation||Yes||Yes||No||Yes (for rural floors); no (for frontier floors)||Yes||Yes||Yes|
|Focuses on markets rather than individual facilities||No||Yes||Yes||No||No||Somewhat||Yes|
|Avoids use of hospital-specific criteria or costs||No||Lugar counties only||Not completely||Yes||No||Yes||Yes|
be more appropriately made by CMS, given the level of complexity of the administrative details involved in implementation. This includes decisions on appropriate thresholds and on whether smoothing should be implemented as positive and negative adjustments, or as positive adjustments only to be offset with a larger national budget neutrality factor.
Although the committee only had time to conduct smoothing simulations on the wage index as applied to IPPS hospitals, any of the methods analyzed in this chapter could be applied to other Part A providers. Commuter pattern–based adjustments could also be implemented for smoothing GPCI values. The key to implementation across different types of providers is the availability of commuter data for the right industry subsector—for example, for nursing homes, for ambulatory care, or for all health care workers.
Recommendation 4-1: The committee recommends that wage indexes be adjusted using formulas based on commuting patterns for health care workers who reside in a county located in one labor market but commute to work in a county located in another labor market.
The committee examined four approaches to adjusting the boundaries of labor markets: the current reclassification and exceptions systems, two county-based smoothing methods—one based on contiguous counties and the other based on commuting patterns—and a hospital-specific geospatial method.
The current system of reclassifications and exceptions is administratively burdensome, and it also relies on individual hospital cost data. Exceptions based on individual provider data are not consistent with the committee’s fundamental principle that geographic indexes should adjust for market-level variation in the price of inputs facing providers rather than the cost of inputs that providers actually incur.
Contiguous-county smoothing has some advantages over the hospital-specific adjustments, but it also has significant problems. The method is based on county adjacency using a pre-set tolerance for adjacent differences, and smoothing results are very sensitive to that tolerance level. The committee noted that the contiguity smoothing method does not rest on direct evidence that the adjacent counties actually operate in an economically integrated area. To be more specific, there is no assurance that smoothed adjacent counties compete for labor in the same market.
The committee recommends commuting pattern–based smoothing because it is anchored in a solid conceptual framework linking commuting with economic integration and therefore with labor markets. It is also consistent with the way metropolitan statistical areas (MSAs) are defined. Commuting patterns of health care workers are an indication of overlap and economic integration of labor markets across their geographically drawn boundaries. Implementing the adjustments based on commuting patterns of all health care workers, as opposed to hospital workers only, would incorporate the contribution of labor employed by physician offices and other health providers, and it would acknowledge a growing degree of integration in the workforce across clinical practice settings.
The committee is in favor of targeting smoothing adjustments to areas with significant wage cliffs and strong evidence of economic integration. Therefore, the committee is generally in favor of establishing thresholds to identify counties that should be eligible for smoothing
adjustments. Rules for specific thresholds, however, are more appropriately developed by the Centers for Medicare and Medicaid Services (CMS).
The committee is not making a recommendation on whether to apply smoothing in both directions or apply smoothing for positive adjustments only. An advantage to applying smoothing in both directions is that the adjustments tend to cancel each other out within a region, and there is less need to underwrite the cost of the adjustments with a national budget neutrality factor applied to all providers. An advantage to limiting smoothing to positive adjustments only is that it will be less disruptive to the current payment system and perhaps require less of a phase-in.
The committee is in favor of adjustments based on outmigration rather than in-migration patterns to address the issue of hospitals competing for workers in surrounding higher-wage areas and because there is precedent in using an outmigration adjustment. However, the full range of options should be reviewed by the U.S. Department of Health and Human Services (HHS) and CMS, given the level of complexity of the administrative details involved in implementation.
Recommendation 4-2: The committee’s recommendation (4-1) is intended to replace the system of geographic reclassification and exceptions that is currently in place.
The committee believes that this recommendation (4-1), if adopted, should improve the accuracy of the wage index and reduce the need for reclassifications and exceptions based on individual provider costs.
The committee regards frontier state index floors as policy adjustments rather than as adjustments intended to improve index or market accuracy. While the committee is charged with reviewing the geographic payment adjusters for accuracy, it also recognizes that some parts of the current administrative system of reclassifications and exceptions may serve other policy goals. Thus, while formula-based smoothing is recommended as a replacement for all types of reclassification, Lugar counties, the current set of Section 505 outmigration adjustments, and the rural floors, smoothing is not a replacement for frontier state floors, nor will it accomplish some of the policy objectives embedded in the special considerations that are now given to sole community providers and rural referral centers to help them qualify for reclassification. In keeping with its objective to separate technical price adjustments from policy interventions, the committee will consider the policy goals addressed by frontier state floors and the policy goals embedded in special rural hospital considerations as part of its phase 2 report.
The committee’s recommendations for revising the wage index and the Geographic Practice Cost Indexes (GPCIs), adopting more accurate labor markets, and smoothing labor market boundaries based on commuting patterns should reduce the need for special exceptions. Special circumstances may still arise related to market-level inaccuracies that could create a need for administrative exceptions. The committee believes that such exceptions should be restricted to addressing market-level issues, however, and not for individual provider adjustments based on individual provider circumstances. The need for any additional adjustments should be assessed in the context of the underlying principles as described in Chapter 1 of this report, including consistency of criteria, market-based rationale to make adjustments, and transparency to stakeholders.
CMS (Centers for Medicare and Medicaid Services). 2004. Medicare program; changes to the Hospital Inpatient Prospective Payment Systems and fiscal year 2005 rates. Federal Register 69(154):48916.
MedPAC (Medicare Payment Advisory Commission). 2007. Report to the Congress: Promoting greater efficiency in Medicare. Washington, DC: MedPAC.
OMB (Office of Management and Budget). 2000. Standards for defining metropolitan and micropolitan statistical areas; Notice. Federal Register 65(249):82228–82238.
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