Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 85
4 Smoothing the Borders of Labor Markets and Payment Areas M edicare adjusts payments to hospitals according to their geographic location in desig- nated 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 varia- tion 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 recom- mended 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. 85
OCR for page 86
86 GEOGRAPHIC ADJUSTMENT IN MEDICARE PAYMENT 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 commut- ing patterns of health care workers. ADJUSTMENT APPROACHES UNDER THE CURRENT HOSPITAL WAGE INDEX 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 reclas- sification 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 request- ing reclassification and higher than the market wage where they are geographically located. MGCRB can also grant requests to re-designate whole nonmetropolitan counties as metro- politan, 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.
OCR for page 87
87 SMOOTHING THE BORDERS OF LABOR MARKETS AND PAYMENT AREAS TABLE 4-1 Types of Administrative Adjustments Under the Current System Based on Based on Based on Commuting Hospital Other Adjustment Patterns Wages 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. SOURCE: RTI analysis of IPPS Impact File as published August 2010 and Final Wage Index Tables for FY 2011. 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 pre- vailing 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.
OCR for page 88
88 GEOGRAPHIC ADJUSTMENT IN MEDICARE PAYMENT 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. ADJUSTMENT APPROACHES CONSIDERED BY THE COMMITTEE The committee sought to recommend a method of addressing labor market border prob- lems 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
OCR for page 89
89 SMOOTHING THE BORDERS OF LABOR MARKETS AND PAYMENT AREAS 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. Contiguous-County Smoothing Background 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 particu- lar 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 adjust- ment 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).
OCR for page 90
90 GEOGRAPHIC ADJUSTMENT IN MEDICARE PAYMENT BOX 4-1 What Are Budget Neutrality Adjustments, and How Are They Computed in the Committee’s Smoothing Models? Both the original wage index and the geographic practice cost indexes (GPCIs) are designed 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 adjust- ments 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 out- migration adjustment to be budget neutral.) The various smoothing simulations performed for the committee also included esti- mations of budget neutrality factors specific to each simulation. The neutrality factors for the commuter based smoothing algorithms, for example, were estimated by calculating the w orker-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.
OCR for page 91
91 SMOOTHING THE BORDERS OF LABOR MARKETS AND PAYMENT AREAS BOX 4-2 How Contiguous-County Smoothing Was Implemented RTI’s contiguous county smoothing model used data for 3,413 Inpatient Prospective Pay- ment 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 statisti- cal 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. Simulations 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 offset- ting 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.
OCR for page 92
92 GEOGRAPHIC ADJUSTMENT IN MEDICARE PAYMENT TABLE 4-2 Results from Contiguous-County Smoothing Modeled on FY 2011 Hospital Wage Index Index-Neutrality Percent of Percent of Adjustment Needed to IPPSa Hospitals Threshold for a County to Qualify Counties Offset Effect of Positive for an Adjustmenta Affectedb Adjustmentc Affected 85% 9.28 6.91 –0.30 90% 23.20 22.00 –1.20 95% 66.83 58.42 –3.80 NOTE: IPPS = Inpatient Prospective Payment System. 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 aggre- gate index value back to 1.00. SOURCE: RTI Analysis of CMS FY 2011 wage index data. 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 per- cent) 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 hospi- tals 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. Review 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 eco- nomic integration so that adjacent areas can be assumed to face similar prevailing wages. Labor markets are also influenced by factors such as topography, transportation, demo- graphics, 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
OCR for page 93
93 SMOOTHING THE BORDERS OF LABOR MARKETS AND PAYMENT AREAS TABLE 4-3 Distribution of Impact from Contiguous-County Smoothing Algorithm on Centers for Medicare and Medicaid Services (CMS) Data, Using a 90 Percent Threshold for Tolerable Wage Index Differences Comparison of Smoothed Index With: Original FY 2011 Final Wage Index Hospital Wage Index (Post-reclassification and (Pre-reclassified) All Other Adjustments) IPPS Facilities IPPS Facilities Impact on Index Value Number Percent Number Percent 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 Total 3,413 100.0 3,413 100.0 NOTE: IPPS = Inpatient Prospective Payment System. 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. SOURCE: RTI analysis of FY 2011 wage index data. 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 General Background 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 com- muting 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 7 CBSAs are further discussed in Chapter 2, Box 2-2.
OCR for page 94
94 GEOGRAPHIC ADJUSTMENT IN MEDICARE PAYMENT (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 patterns 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 com- muting 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 mar- kets. 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).
OCR for page 95
95 SMOOTHING THE BORDERS OF LABOR MARKETS AND PAYMENT AREAS 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 com- muting pattern–based smoothing adjustments that resemble expanded forms of the current outmigration adjustment. Commuting Patterns The 2000 census data set identified 1,596 counties as having a hospital and 2,730 coun- ties 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. Simulations 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 com- muting 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 exam- ple, 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. Simulation Options 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).
OCR for page 102
102 GEOGRAPHIC ADJUSTMENT IN MEDICARE PAYMENT TABLE 4-7 County-Level Effects from Out-Migration County Smoothing on Bureau of Labor Statistics (BLS) Wage Index Values Optional Smoothing Parameters No Minimum 10% Minimum 10% Minimum, Commuting Commuting Positive Threshold Threshold Adjustments Only N % N % N % Counties affecteda Metropolitan areas Increase 213 27% 128 17% 128 17% Decrease 279 36% 95 12% No change 283 37% 552 71% 647 83% 775 100% 775 100% 775 100% Rest-of-state areas Increase 409 50% 279 34% 279 34% Decrease 84 10% 476 59% No change 317 39% 55 7% 531 66% 810 100% 810 100% 810 100% Percent change in index value (excluding effect of budget neutrality factor) Minimum –4.2 –4.2 0.0 10th percentile –0.3 0.0 0.0 25th percentile 0.0 0.0 0.0 50th percentile 0.0 0.0 0.0 75th percentile 0.4 0.1 0.1 90th percentile 2.0 2.0 2.0 Maximum 16.4 16.4 16.4 Estimated budget 1/1.002516 1/1.00236 1/1.00313 neutrality factors (or –0.25%) (or –0.24%) (or –0.32%) a Includes only counties that have at least one Inpatient Prospective Payment System (IPPS) hospital. SOURCE: RTI Analysis of 2000 Census hospital worker commuting data and BLS-constructed hospital fixed-weight index using 30 occupation codes and all-employer hourly wage from May 2010. 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-
OCR for page 103
TABLE 4-8 Hospital-Level Impact of Out-Migration Smoothing on Centers for Medicare and Medicaid Services (CMS) and Bureau of Labor Statistics (BLS) Indexes from Distribution of Percent Change in Index Values Smoothed Versus Unsmoothed CMS Index Smoothed Versus Unsmoothed BLS Index Number of 25th 50th 75th 25th 50th 75th Hospitalsa Min pct pct pct Max Min pct pct pct Max By Location 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 Rural (nonmetropolitan)b 959 –31.87 –7.41 –2.33 –0.83 22.00 –17.44 2.88 8.73 13.98 20.99 All 3,463 –31.87 –2.49 –0.74 0.59 26.30 –17.44 2.69 6.78 11.60 25.13 By Current Wage Index Statusc No adjustments 2,150 –7.62 –1.13 –0.10 0.77 22.00 –15.89 3.40 8.45 12.58 22.43 Lugar hospitals 55 –17.94 –9.60 –5.45 –3.29 10.30 –9.40 –0.96 4.70 8.91 19.61 MGCRB reclassifications 748 –24.66 –9.31 –4.81 –0.96 26.30 –17.44 –1.07 3.32 6.84 23.46 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 Total 3,463 –31.87 –2.49 –0.74 0.59 26.30 –17.44 2.69 6.78 11.60 25.13 NOTE: Smoothing includes effect of budget neutrality adjustment. Smoothing was implemented with no thresholds or positive and negative adjustments. Max = maxium; MGCRB = Medicare Geographic Classification Review Board; Min = minimum; MSA = metropolitan statistical area; pct = percentile. 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. SOURCE: RTI analysis of FY 2011 wage index data, 2000 census hospital worker commuting data, and BLS-constructed hospital fixed weight index using 30 occupation codes and all-employer hourly wage from May 2010. 103
OCR for page 104
104 GEOGRAPHIC ADJUSTMENT IN MEDICARE PAYMENT Average Change in Pre-Reclassified Index Values No Adjustments Frontier State Floor MSA w/Rural Floor MGCRB Reclass Sec 505 Outmigration Lugar County 0 1 2 3 4 5 Percent Change All Counties, No Thresholds Applied 10% Commute Threshold Applied 10% Threshold Applied, Positive Changes Only Average Change in BLS Index Values No Adjustments Frontier State Floor MSA w/Rural Floor MGCRB Reclass Sec 505 Outmigration Lugar County 0 1 2 3 4 5 Percent Change All Counties, No Thresholds Applied 10% Commute Threshold Applied 10% Threshold Applied, Positive Changes Only FIGURE 4-3 Impact of out-commuting smoothing under three design options, computed across hospitals grouped by current wage index exception status. Figure 4-3
OCR for page 105
105 SMOOTHING THE BORDERS OF LABOR MARKETS AND PAYMENT AREAS 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 sys- tems (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 cen- tral 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 imple- mentation 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 dis- tances 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-reclassification 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 adjust- ments on the final wage index compared to the pre-reclassification index. The second column
OCR for page 106
106 GEOGRAPHIC ADJUSTMENT IN MEDICARE PAYMENT TABLE 4-9 Index Wage Cliffs: Nearby Hospital Pairs with Large Difference in Wage Index Values, Before and After Inverse-Distance-Weighted (IDW) Smoothing Number of IPPS Hospital Pairs with Wage Index Differences of 0.10 Points or More Under Under Final Under Pre-reclassification Distance Between Pre-reclassification Post-reclassification Wage Index with IDW Hospitals (miles) Wage Index Wage Index Smoothing 1 0 1 0 5 23 17 0 10 152 60 0 25 1,709 614 316 NOTE: IDW = inverse distance weighting; IPPS = Inpatient Prospective Payment System. Hospitals can be counted more than once. Geospatial smoothing implemented with ARC-GIS version 10.0 software, with the default weights set proportional to the inverse of the square of the distance between hospital pairs within a fixed maximum search radius of 25 miles. SOURCE: RTI analysis of FY 2011 wage index data from CMS. 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.” Review 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 com- putations 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 pro- vider’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).
OCR for page 107
107 SMOOTHING THE BORDERS OF LABOR MARKETS AND PAYMENT AREAS TABLE 4-10 Distribution of Impact of Inverse-Distance-Weighted (IDW) Smoothing on FY 2011 Centers for Medicare and Medicaid Services (CMS) Wage Index for Inpatient Prospective Payment System (IPPS) Facilitiesa IDW-Smoothed IDW-Smoothed Index Compared to Index Compared with Final (Post-reclassified) CMS Final Indexb Indexb Pre-reclassified Index Impact on Index Value Number Percent Number Percent Number Percent 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% Total 3,391 100.0% 3,391 100.0% 3,391 100.0% NOTE: Geospatial smoothing implemented using ARC-GIS 10.0 software with default weights set proportional to the inverse of the square of the distance between hospital pairs, within a fixed maximum search radius of 25 miles. 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. SOURCE: RTI Analysis of FY 2011 wage index data. chosen point, on the premise that proximity is a good proxy for economic integration. The com- mittee recognizes that labor markets are highly influenced by location, but topography, transpor- tation, 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
OCR for page 108
108 TABLE 4-11 Summary Evaluation of Alternative Approaches for Refining Labor Market Boundaries Current Administrative Approaches Alternative Approaches GIS: Out- Inverse Contiguous- Commuting Committee’s Stated Evaluation Hospital County migration Distance County Pattern–Based Objectives Reclassification Re-designation Adjustment Index Floors Weighting Smoothing Smoothing Is plausibly linked to the notion of Yes Yes Yes No Possibly Possibly Yes improved accuracy in labor market definitions Uses computations that are Yes Yes Yes Yes No Yes Yes transparent and reproducible by the provider community Relies on data that are publicly Yes Yes Yes Yes Yes Yes Yes available, reliably produced, and periodically updated Is systematic and formula-based, No Yes Yes Yes Yes Yes to minimize the need for individual Lugar counties reviews or exceptions processes only Is made budget-neutral in Yes Yes No Yes (for rural Yes Yes Yes implementation floors); no (for frontier floors) Focuses on markets rather than No Yes Yes No No Somewhat Yes individual facilities Avoids use of hospital-specific criteria No Lugar counties Not Yes No Yes Yes or costs only completely NOTE: GIS = geographic information system.
OCR for page 109
109 SMOOTHING THE BORDERS OF LABOR MARKETS AND PAYMENT AREAS 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 adjust- ments 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. COMMITTEE RECOMMENDATIONS 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 work- force 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
OCR for page 110
110 GEOGRAPHIC ADJUSTMENT IN MEDICARE PAYMENT 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 smooth- ing 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 Ser- vices (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 adjust- ments 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 Prac- tice 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, includ- ing consistency of criteria, market-based rationale to make adjustments, and transparency to stakeholders.
OCR for page 111
111 SMOOTHING THE BORDERS OF LABOR MARKETS AND PAYMENT AREAS REFERENCES 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 effi- ciency 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.
OCR for page 112