Medicare Payment Generosity and Access to Care

Noname manuscript No. (will be inserted by the editor)

Medicare Payment Generosity and Access to Care Christopher S. Brunt · Gail A. Jensen

Received: date / Accepted: date

Abstract All payments to physicians under Medicare Part B are adjusted to reflect geographic differences in practice costs. The methods used for this adjustment, and temporary price floors imposed by Congress, have created longstanding systematic under and overpayment across physicians, whereby some are routinely underpaid while others are routinely overpaid. Using a nationally representative 2008 survey of physicians, this study examines whether the relative generosity of Medicare influences beneficiary access to care. We find that in areas where Medicare payments are more generous physicians are more likely to accept new Medicare patients, whereas in less generous areas, they are less likely. Our estimated models suggest that if Medicare could eliminate the systematic biases inherent its payment formula, it would see a net improvement to access to care under Medicare Part B. Keywords Access to Care · Medicare Part B · Physicans · Payment JEL Classifications I10 I12 I18 This is a pre-production version of the manuscript published in Journal of Regulatory Economics (2013) 44:215-236. The final publication is available at http://link.springer.com/ article/10.1007%2Fs11149-013-9218-7. Christopher S. Brunt Department of Finance and Economics, Georgia Southern University P.O. Box 8152, Statesboro, GA 30460 Tel.:(912) 478-8011 E-mail: [email protected] Gail A. Jensen Wayne State University, Institute of Gerontology, Department of Economics 87 East Ferry Street, Detroit, Michigan 48202 Tel.: (313) 664-2622 E-mail: [email protected]

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Christopher S. Brunt, Gail A. Jensen

1 Introduction In 2011 Medicare provided health insurance to 49 million elderly and disabled Americans, most of whom (37 million) were enrolled in traditional Medicare, known as Medicare Parts A and B (Gold et al. 2012). Part B, which covers physician visits, outpatient services, preventive services, and home health care, accounted for 29% of all spending under Medicare in 2011 (Kaiser Family Foundation 2011). Medicare sets the fees it pays for services under Part B. Its payment formula accounts for geographic variation in the cost of healthcare workers and medical office space, to ensure that doctors are compensated fairly for differences in the cost of operating a medical practice. Medicare uses 89 distinct geographic areas to adjust its fees for input price differences. Because many of these areas are quite large, e.g., as big as entire states, they do not capture all of the input price variation that actually exists across local labor and rental markets. A recent Government Accountability Office (GAO) study on the accuracy of Medicare’s geographic adjustment factors found many of the payment areas have counties within them where a gap of 5% or more exists between Medicare’s geographic adjustment factor and physicians’ actual input prices (GAO 2007). As a result of inaccuracies in the formula, in many payment localities the prices Medicare sets for routine physician services are effectively more generous in some communities than they are in others. To illustrate, consider Statesboro Georgia, home to Georgia Southern University, and Savannah Georgia, about 45 minutes east of Statesboro, both of which are in the same Medicare payment locality. Medicare’s geographically adjusted payment rate in 2008 for a routine office visit with an established patient (CPT code 99213) was the same in both places, $60. Yet the salaries of highly trained professionals, healthcare workers, such as nurses and billing specialists, as well as office rents are about 12 to 17% higher in Savannah than they are in Statesboro. Given two physicians with identical practices, one in Savannah, the other Statesboro, the Savannah physician incurs higher expenses, thereby earning less on each visit with a Medicare patient. If Medicare’s payment formula had instead recognized these geographic differences in input costs, then (based on our calculations) Medicare would have paid physicians in Savannah $61 for a routine office visit (CPT code 99213) and physicians in Statesboro $53. This same phenomenon occurs within many other Medicare payment localities, as well. This paper investigates whether differences in Medicare’s payment generosity that arise from imprecision in its geographic adjustment factors influence physicians’ decisions to accept new Medicare patients. Medicare works better for beneficiaries when physicians are willing to accept them as patients. Economic theory suggests that lower Medicare fees may discourage physicians from accepting new Medicare patients, or lead them to reduce the quantity or quality of care they provide to Medicare patients (Sloan, Mitchell and Cromwell 1978; McGuire and Pauly 1991; Gillis and Lee 1997; Brunt and Jensen 2012). As long as physicians can choose between different types of patients, those that come with lower payment rates may be relegated to the back of the queue.

Medicare Payment Generosity and Access to Care

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Two previous studies provide evidence that Medicare’s fee level has a positive and significant impact on physicians’ willingness to take on new Medicare patients (Rodgers and Musacchio 1983 and Gillis and Lee 1997). However, their findings are based on data from 1978, 1991, and 1993 under a completely different payment structure. In 1998 Medicare began using a new formula to set Part B fees, one that continues today, and nowadays most privately insured non-Medicare patients are under managed care, so physicians may not have as much latitude as they once had in setting prices for their privately insured patients. Thus, there is a need to re-examine the linkage between payment generosity and physicians’ willingness to accept new Medicare patients. The issue of payment generosity and access to care is important for three reasons. First, assuring beneficiaries have access to high-quality care is a major goal of the Medicare program (Medicare Payment Advisory Commission 2011). Although beneficiaries have better access to physicians than individuals with Medicaid, they have less access than do privately insured patients, according to some measures. For example, in a 2008 survey of U.S. physicians, 28% said they were not accepting new Medicaid patients, 14% said they were not accepting new Medicare patients, and 4% said they were not accepting new privately insured patients (Cunningham 2010). Other data suggests that problems finding a new primary care physician are becoming more frequent among beneficiaries. For example, in 2011, among Medicare beneficiaries who say they tried to get an appointment with a new primary care physician, 35% reported a problem finding one, 23% said the problem was a “big” one and 12% said it was “small” (MedPac 2012). In contrast, in 2008 29% reported a problem finding a primary care physician, 18% said the problem was big, and 10% said it was small (MedPac 2012). The second reason is that policymakers sometimes choose to reduce payments to providers as a way of containing Medicare spending. For example, since 1989 the Center for Medicare and Medicaid Services (CMS), which administers Medicare, has been given a mandate of “budget neutrality” under Medicare Part B. This implies that observed changes in coding, service delivery, or the practice of medicine cannot increase Part B expenditures each year by more than $20 million from the level of spending that would have occurred without the change. The Balanced Budget Act of 1997, further compounded potential cuts through the so-called sustainable growth rate (SGR) policy, which stipulates that after accounting for spending increases justified by growth in the number of Medicare enrollees, by changes in the law, and by changes in the cost of operating a typical medical practice, the annual percentage increase in Part B expenses per beneficiary cannot exceed the growth rate in the U.S. gross domestic product. Despite annual “patching” of the SGR policy since 2003, CMS has had to respond to their budget neutrality requirement by lowering real payments to physicians each year through updates to the yearly conversion factor. Thus, examining the growth rate of physician practice costs, as measured by the Medicare Economic Index, in comparison to the growth rate of the Medicare yearly conversion factor, between 1991 and

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2008, costs for physicians grew by 150%. Meanwhile fees paid to physicians have increased by only 140%. Given the stunted growth in physician fees relative to inflation in the cost of operating a medical practice, policymakers need to know whether paying physicians less generously will result in more physicians turning away new Medicare patients, and if so, the slope of this gradient. Their ability to achieve Medicare savings through reduced fee updates while maintaining ample access to services will be undermined if this gradient is negative and steep. The third reason is that a recent Institute of Medicine (IOM) report, Geographic Adjustment in Medicare Payment - Phase I: Improving Accuracy, Second Edition (IOM 2012), has recommended that Medicare expand its number of geographic payment areas from 89 to 441, and define areas on the basis of metropolitan statistical areas (MSAs) and statewide non-MSAs, so that these payment areas more closely reflect actual labor markets (IOM 2012, pp. 7-8). The recommendation recognizes there are inaccuracies in the current geographic adjustment factors. In this paper we examine how these inaccuracies affect a physician’s decision to accept new Medicare patients and whether, on balance, eliminating the inaccuracies would improve or hamper access to physician services. 2 Background Medicare Part B fees are set using a Resource-Based Relative Value System (RBRVS), which attempts to pay physicians in accordance with their actual costs of providing a service at their particular location. Under the formula, each Medicare fee has three components: one that compensates the physician for his/her work effort, called Physician Work Relative Value Units (RVUs), another that compensates for practice-related expenses, called Practice Expense RVUs, and a third that compensates for malpractice expenses, called Professional Liability Insurance RVUs. Each of these quantities is adjusted for geographic differences in the cost of that component, and there is a “conversion factor” (the same across all geographic areas) that converts the total RVUs for that service into a dollar price. More formally, Medicare’s formula for a particular service is given by: Medicare Fee = cf × (w × Gw + e × Ge + i × Gi )

(1)

where cf is a conversion factor, w measures the physician work RVUs for that service, e measures the practice expense RVUs for that service, i measures the professional liability RVUs for that service, and Gw , Ge , and Gi are the respective geographic practice cost indices (GPCIs) used as adjustment factors. Within a specific payment locality, the adjustment factors Gw , Ge , and Gi and cf are uniform across all services. The RVUs, w, e, and i, on the other hand, are constant across all payment localities, but vary depending on the service. (The formula in equation (1) is actually a simplified version of the RBRVS formula, in that it ignores the distinction between facility and non-facility prices,


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the 10% bonus for primary care physicians practicing in designated underserved areas, the 10% bonus for primary care services provided by primary care physicians anywhere (effective January 1, 2011), and the 10% bonus for general surgeons practicing in underserved areas (effective January 1, 2011). Medicare’s geographic adjustment factors, Gw , Ge , and Gi vary across 89 distinct localities, which altogether cover the entire US. This has been the case since 1997. Thirty-four states currently have payment localities comprised of the entire state, while 16 states have multiple localities within their borders (GAO 2007). Medicare is required by law to review its geographic adjustment factors every three years, and update them using more recent data, if needed. Payment distortions arise because the actual variation that exists across the US in the value of physician time and the cost of running a medical practice is much greater than what can be captured with just 89 areas, and because even within the 89 localities the entire magnitude of the variation is not taken into account through Medicare’s formula. Within a given payment locality, physicians whose labor costs or office rental costs are higher than their locality-wide average values end up with less generous Medicare payments than do physicians whose costs are below their locality-wide average values. Compounding problems originating from the broad size of localities is the legislated requirement that CMS only take into account 25% of the observed deviation in the physician work component’s geographic adjustment. Thus all localities exhibit systematic over- or under-payment to physicians.

3 Previous Literature Our analysis builds on two strands of prior research. The first are studies that examine how Medicaid or Medicare fee levels influence physicians’ willingness to treat new Medicaid or Medicare program beneficiaries. Most research in this strand focused on Medicaid. Generally, previous studies have found that low Medicaid payment rates are a significant factor in physicians’ decisions to accept Medicaid patients, although the nature of the relationship has varied across studies (Long et al. 1986; Cohen 1993; Adams 1994; Decker 1993; Showalter 1997; Cunningham and Nichols 2005; Shen and Zuckerman 2005; Decker 2007). In most of the studies, Medicaid payment generosity was measured as the ratio of Medicaid-to-Medicare fees for a given service or bundle of services. One of the most thorough was Decker (2007), who analyzed physicianlevel data from the 1989, 1993, 1998 and 2003 National Ambulatory Medical Care Survey. She found that an increase of 0.35 in the Medicaid-to-Medicare fee ratio (which corresponds to raising the mean ratio to unity) would raise the fraction of physicians who participate in Medicaid by about one-quarter, suggesting a strong response to Medicaid fees. As mentioned earlier, only two studies have investigated the effects of Medicare reimbursement on physicians’ willingness to accept new Medicare patients. Both found that acceptance rates were indeed sensitive to payments. Rodgers and Musacchio (1982) measured Medicare’s fee generosity as the ratio

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of Medicare’s total payment in 1978 for an office visit and a follow-up visit to a cross-sectional cost-of-living index also measured in that year. They found that Medicare payment had a significant and positive effect on a physician’s decision to accept new beneficiaries, with an estimated price elasticity of 0.384. In words this means when Medicare fees were 10% higher, the probability the physician accepts Medicare patients was 3.84% higher. Gillis and Lee (1997) analyzed data from the American Medical Association’s annual surveys of physicians in 1991, 1992, and 1993. Using data from several sources, including actual Part B claims, they constructed a Laspeyres index of Medicare reimbursement levels, and then estimated models by physician specialty for a physician’s acceptance of new Medicare patients. For physicians in general practice and family practice, they found that Medicare fees had significant positive effects on acceptance decisions. Evaluated at the sample means, for example, a 10% increase in the fee index was associated with a five point increase in the percentage of physicians accepting new patients. Among internists the fee effect was positive but insignificant. While a few other studies have examined physicians’ willingness to accept new Medicare patients, they did not consider the effects of Medicare fees (Hasnain, Hibbard and Weeks 1992; Damiano et al. 1997; Chou et al. 2007). The second strand are studies that examine how pricing distortions under Part B affect the care physicians provide to beneficiaries. Two aspects of this issue have been studied. Hadley et al. (2010) investigated how pricing distortions influence the volume of services physicians provide to Medicare patients. They first constructed a measure of the generosity of Medicare fees and then related the measure to eight specific services that physicians often provide to older adults (office visits, hospital visits, consultations, and various cardiac-related diagnostic tests). They measured fee generosity as the difference between Medicare’s actual fee for a standard office visit with an established patient (i.e., a visit with CPT code 99213) and the fee Medicare would have assigned had it instead used more geographically-detailed input price data to construct Gw and Ge . Using data from two surveys of physicians (conducted as part of the Community Tracking Study [CTS]) linked with Medicare fee-for-service claims data for each physician, they found that the annual quantity of each service provided was positively related to fee generosity. In other words, the higher the relative profit the physician was earning under Medicare, the more of each service he or she supplied over the course of a year. Brunt and Jensen (2012) examined whether pricing distortions in Medicare’s fee schedule influence how satisfied beneficiaries are with their quality of care and access to services. Using data from the Medicare Current Beneficiary Survey (MCBS) and other sources, they classified MCBS-respondents according to whether they live where Medicare’s Part B payment formula is “favorable” or “unfavorable” to providers. They defined an area as favorable if Medicare’s geographic adjustment factors, Gw and Ge , were both higher than they should have been, based on more geographically-detailed data, and as unfavorable if Gw and Ge were both lower than they should have been. They then compared beneficiary satisfaction across favorable and unfavorable areas.


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For each of six different aspects of care (overall quality of care, availability of services on nights and weekends, ease of getting to the doctor, follow-up treatment, availability of specialists, and ease of obtaining answers to questions over the telephone) they found that beneficiaries in payment-favorable areas assigned significantly higher satisfaction ratings than did beneficiaries in payment-unfavorable areas. Like these recent studies, we first measure the inaccuracy in Part B fees arising from Medicare’s use of very large geographic payment areas and legislatively imposed restrictions on the variation in payment, and then examine how this payment inaccuracy relates to the choices physicians make regarding Medicare patients. We now turn to the data and methods used in our analysis.

4 Data and Methods Our main dataset is the 2008 Restricted Health Tracking Physician Survey (HTPS), a nationally representative survey administered to 4,720 physicians in that year. The survey was restricted to U.S. physicians who were providing direct patient care for at least 20 hours per week, and excluded federal employees, medical residents and fellows, and foreign medical school graduates only temporarily licensed to practice in the U.S. The HTPS collected information on the characteristics and compensation arrangements of each surveyed physician and their practice, as well as basic information on the physician’s specialty and board certification status from the American Medical Association. To capture local healthcare and labor market characteristics we supplement the HTPS with several other data sources. Data on county characteristics are drawn from the 2010 Area Resource File, data on local median occupational wages in 2008 are drawn from the Bureau of Labor Statistics (BLS 2008), and 2008 data on local median rents for two bedroom apartments are drawn from the Department of Housing and Urban Development (HUD 2008). Data on Medicare’s geographic adjustment factors for 2008 are drawn from the Center for Medicaid and Medicare Services (CMS 2007). Our unit of observation is a physician, and we restrict our focus to 3,304 physicians who were practicing in either a solo or group practice setting and who reported complete data on variables used in the analysis1 . Table 1 reports summary statistics on the characteristics of these 3,304 physicians and their practices. 1

161 physicians were not included in our final analytic sample because their practice setting was at an HMO, 341 were not included because they practiced at a medical school, 603 were not included because they practiced medicine at a hospital, and 190 were not included because they practiced medicine at some other undefined location. 53 were excluded because they did not report compensation arrangements, and the remainder were excluded because they reported not knowing the attributes of their practice (e.g., the percentage of patients with Asthma or Heart Failure).

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4.1 Dependent Variable The dependent variables used for the analysis originate from the physician’s response to the following question: “Is your practice accepting all, most, some, or no new patients who are insured through Medicare, including Medicare managed care patients?” One response was allowed, either “all,” “most,” “some,” or “none.” As shown in Table 1, 51% of physicians said they are accepting all new Medicare patients, 18% said they are accepting most, 14% said they are accepting some, and 17% said they are accepting no new Medicare patients.

4.2 Key Variables of Interest The physician work adjustment factor used in Medicare’s payment formula, Gw , is based on national wage bill weighted decennial census median wage data for six non-physician occupations. Besides the errors introduced through the use of older data for the construction of this adjustment, Congress, in an effort to promote a more equitable distribution, imposed a 25% deviation restriction on all work adjustment factors in conjunction with the implementation of the RBRVS. This restriction forces Medicare to only take into account 25% of the observed deviation from the national average when calculating a localityspecific Gw and causes physicians in areas with below average cost of living to be systematically overpaid and physicians in areas with above average cost of living to be systematically underpaid. Further compounding the inherent bias associated with these constraints is the Balance Budget Act of 2003, which attempted to provide greater levels of access to care by imposing a price floor of 1 for all areas with below average cost of living.2 In this study we first calculate Gw and Ge more accurately, denoting our measures as G∗w and G∗e , and then measure Medicare’s pricing distortions as the difference between its observed adjustment factors, Gw and Ge , and our more accurate measures, G∗w and G∗e . To aid in interpreting these variables, let us find the difference between the observed Part B payment and the same payment constructed using the more accurate geographic adjustment factors for any service provided under Part B using equation (1). Medicare Fee − Medicare Fee∗ = cf × (w × Gw + e × Ge + i × Gi ) − cf × (w × G∗w + e × G∗e + i × Gi ) Eliminating the professional liability component, given that we have no better method for determining it, and distributing the conversion factor through, the difference becomes: Medicare Fee − Medicare Fee∗ = 2 During the period of our sample, this floor was in effect for all localities with a work adjustment factor less than 1. For the 2012 update, the floor is restricted to the frontier states of Montana, Nevada, North and South Dakota, Wyoming, and Alaska (which has a 1.5 work floor).


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cf (w) × (Gw − G∗w ) + cf (e) × (Ge − G∗e ) Given that Gw and Ge represent percentage adjustments from the national fee schedule, this implies that for any Part B service our generosity measure for the work (practice expense) component is the conversion factor adjusted physician work (practice expense) relative value unit percentage over- or underpayment. Thus when (Gw − G∗w ) is positive (negative) Medicare overpaid (underpaid) the physician in 2008 on his/her own work effort for all services billed under Part B in that year, and the higher (lower) the value the more the overpayment (underpayment). (Ge − G∗e ) has a parallel interpretation for Medicare’s reimbursement of a physician’s practice expense. Using the Bureau of Labor Statistics, Occupational Employment Statistics data for the Standard Occupational Codes used by CMS in its calculation of the physician adjustment factor, we follow a method similar to CMS and Hadley et al. (2009) to construct the more accurate physician work adjustment factor, Gw , by: (1) using updated occupational median wage data from 2008 (rather than from 2000, which is what CMS used), (2) using smaller geographic areas than the 89 areas used by CMS (BLS-defined metropolitan statistical areas (MSAs) and non-MSAs instead of state level adjustment), (3) eliminating the 25% deviation restriction, and (4) eliminating the physician work price floor. From this more accurate G∗w , we then calculate the distortion in Medicare’s physician work RVU over- and under-payment by subtracting the more accurate generated G∗w from the observed Gw . CMS’ practice expense Ge consists of three components weighted based on their share of the Medicare Economic Index: (1) an employee wage index, (2) a rent index, and (3) and office supply index. The employee wage index is constructed using a nationally weighted average of median hourly earnings for physician support staff such as office workers and nurses. The rent index is constructed for lack of better data, using HUD median two bedroom unit fair market values in comparison to the national median fair market value. Office supplies are assumed to be purchased at a national level and have uniform index of one applied across all localities. We follow a similar approach to CMS to create a more accurate practice expense adjustment factor, denoted G∗e , improving on its accuracy by: (1) using more accurate occupational median wage data from 2008, (2) using smaller geographic areas than CMS (BLS MSAs and non-MSAs instead of state/urban/rural level adjustment) for the employee wage index component, (3) using 2008 HUD fair market values unavailable to policy makers at the time of the fee update, (4) using smaller geographic areas for the rent index component (county level data is used instead of state level and/or urban-rural data). From this more accurate G∗e , we calculate Medicare’s distortion in the measurement of Ge as the difference between Ge and G∗e . Given that much of the variation in our key explanatory variables stem from legislatively introduced imperfections, mainly the price floor on the physicians work component weight, and the fact that the work component weight only takes into account 25% of the observed areas median wage deviation from the

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national median, we anticipate that factors related to cost-of-living will in part influence the degree to which physicians are over or underpaid. The correlation coefficients between income per capita and our key generosity measures are -0.56 and -0.34 for work generosity and practice expense generosity, respectively. Within states, we observe lower mean generosity across quartiles of income per capita. For example, within Illinois mean work generosity decreases from 9.45 to -6.67 for physicians located in areas compensated in the second quartile of income per capita to the third quartile. We now turn to a description of the control variables used in our analysis.

4.3 Control Variables Each model includes controls for physician characteristics, practice size, compensation arrangements, patient characteristics, including racial composition and morbidity characteristics, area characteristics including whether the county was designated as a Health Professional Shortage Area (HPSA), county population density, census region, and the number of physicians per capita in the county. State and Physician AMA specialty fixed effects are also included in each model. Complete descriptions of all the variables along with summary statistics by dependent variable outcome are provided in Table 1. The typical physician within the sample is male, has approximately 15 years in practice, and works at a practice where he is a full owner. The physician typically treats patients with asthma, diabetes, and depression, but not chronic heart failure. As can be seen in Table 1, there is not a large degree of variation in physician and area characteristics across the dependent variable outcomes with the exception of the work and practice expense differences, (Gw −G∗w ) and (Ge −G∗e ). The overall mean of the work generosity measure is 4.33. This implies that physicians in this sample, on average, were overcompensated on conversion factor adjusted work relative value units by 4.33%. The fact that the survey adjusted mean is not zero is not surprising given the work component Gw price floor of 1 which was in place at the time of this survey, 2008. The overall mean of the practice expense component generosity measure is -16.33% implying that in regards to practice expense physicians, on average, were undercompensated. The fact that it is less than zero can in part be explained by the broad size of most geographic areas used for payment localities (89 in all), which are often are entire states or a state split into one urban and non-urban area. At the means, physicians responding that they are accepting no new Medicare patients, are compensated for each RVU with a Medicare generosity of only 1.80% above their cost with respect to the physician work component, and are underpaid on the practice expense component by 18.62% for each RVU. Comparing these physicians to those reportedly accepting all new Medicare patients, we see that physicians in the “all” category are compensated substantially more on each


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RVU with a mean of 5.049% on each physician work RVU, and are underpaid to a lesser degree on the practice expense component.3

4.4 Regression Model Given the ordered nature of the response variable, we first estimate an ordered logit for the physician’s response to the question, “Is your practice accepting all, most, some, or no new patients who are insured through Medicare, including Medicare managed care patients?” We then estimate two dichotomous logit models, one for each of the important extreme values of accepting ‘no’ new Medicare patients vs. accepting at least some, and accepting ‘all’ new Medicare patients vs. accepting less than all. We now present the results of the primary analysis.

5 Results A summary of the coefficient estimates for key reimbursement generosity measures in the ordered logit and logit regressions are reported in Table 2. This table summarizes the results from six different models that were estimated. Models (1), (3), and (5) contain only the physician specialty controls and state fixed effect controls, whereas models (2), (4), and (6) contain the full set of controls listed in Table 1.4 Columns (1) and (2) report the coefficients of (Gw − G∗w ) and (Ge − G∗e ) in the ordered logit model, columns (3) and (4) report the coefficients of these variables in the logit models for accepting “all” new Medicare patients, and columns (5) and (6) report the coefficients of these variables in logit models for accepting “no” new Medicare patients. In all six models the coefficients on (Gw − G∗w ) and (Ge − G∗e ) are either independently or jointly significant at conventional levels, with (Gw −G∗w ) and (Ge − G∗e ) significant at the 1% in models (1) and (2), significant at the 2% level in models (3) and (4), and significant at the 3 and 9% levels in models (5) and (6), respectively. These significant results indicate an increased probability of accepting new Medicare patients with greater payment generosity, and reductions in the probability of accepting no new Medicare patients. The marginal effects of (Gw − G∗w ) and (Ge − G∗e ) at the sample means, and their significance levels are presented in Table A-1 for models (2), (4), and (6). To aid in interpreting the marginal effects, we computed the predicted probabilities across the range of our key generosity measures using Statas margins command. For work RVU percentage overpayment under model (2), the predicted average marginal effects indicate that as RVU payment generosity increases from the least generous (-30%) to neutral generosity (0%), the probability that a physician accepts no new Medicare patients declines by 4.7 3 4

The mean practice expense underpayment for this group was 15.431. Full regression results are available from the author upon request.

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percent. This movement translates directly to the “all” Medicare patients category with increases in the probability of accepting all new Medicare patients by 7.6 percent. The middle categories of accepting “some” and “most” new Medicare beneficiaries also see significant declines.5 For (Gw − G∗w ), the marginal effects imply that movement from neutral generosity to most generous (25%) result in 3.4, 2, and 1.3 percent declines in the probability of selecting ‘none,’ ‘some,’ and ‘most,’ respectively. This implies that if the most underpaid physicians were compensated with the greatest levels of generosity, the probability that they would accept no new Medicare patients would decline by 8 percent, and the probability that they would accept all new Medicare patients would increase by 14.2 percent. For (Ge − G∗e ), the marginal effects imply that a movement from the least generous to neutral, on each RVU, changes the probability of accepting ‘none,’ ‘some,’ ‘most,’ and ‘all’ new Medicare patients by -3.8, -1.8, -0.9, and 6.1 percent, respectively. The logit models for accepting “all” new Medicare patients and “no” new Medicare patients in comparison to the alternative grouped categories produced similar results to the ordered logit model. Specifically, moving from least generous to neutral payment results in a 6.2% increase in the probability that a physician is accepting all new Medicare patients under model (4), and a 6% decline in the probability of not accepting any new Medicare patients under model (6). The movement from neutral payment to most generous implied an additional 5% increase in the probability of accepting all new Medicare patients under model (4), and an additional 4% reduction in the probability of not accepting any new Medicare patients under model (6).

6 Sensitivity Analysis To ensure the robustness of the findings, a number of alternative specifications and sensitivity tests were performed. The results of these robustness tests were comparable to and in support of the findings reported in Table 2. Our first major concern was the potential endogeneity of some of the variables, such as those describing patient characteristics. If the physicians practice routinely treats patients with diabetes or heart failure, such patient characteristics may be endogenous with the decision to accept Medicare patients, since Medicare patients tend to have more chronic conditions due to age. When we re-estimated the models excluding all patient morbidity controls the coefficient estimates on the key generosity measures were still significant and comparable to our original models. Our second concern was potential multicolinearity between our key generosity measures and other explanatory variables, particularly variables that are correlated with urban settings. We tested for the presence of multicoliniary using variance inflation factor (VIF) tests. The VIF tests did not indicate colinearity problems between these variables. As a further check for multicolinearity 5 The category “some” and “most” have predicted reductions in probabilities of 2.2 and 0.8 percent, respectively, as generosity moves from least generous to neutral.


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we estimated linear regression models for both (Gw − G∗w ) and (Ge − G∗e ) as a function of other explanatory variables in these models. Appendix Table A-2 reports the results. (See Columns (1) and (2) in the Table.) Given the significance of factors likely to be correlated with practicing in an urban setting, such as the percentage of patients in each minority group, and population density, we were concerned that the generosity measures might be picking up some urban effects rather than payment generosity, per se. To investigate this possibility, we re-estimated the ordered logit model several times, each time dropping an additional set of explanatory variables until there was nothing left in the model except (Gw − G∗w ) and (Ge − G∗e ), physician specialty, and state fixed effects. Columns (3) through (11) in Appendix Table A-2 indicate that the coefficients of interest remained stable and significant upon the inclusion/exclusion of all other control variables. Our third concern revolves around potential measurement error among some of the survey respondents. While a solo practicing physician should know the degree to which their practice is accepting “none,” “some,” “most,” or “all” new Medicare patients, it is not clear that an employed physician, or even a physician at a large practice would accurately be able to gauge the difference between “some and “most. This could give rise to measurement error in the dependent variable for some types of physicians. To ensure that our results are not dramatically affected by measurement error, we re-estimated the model on the subsample of physicians who reported they were full practice owners. We found robust and significant results comparable to what we reported in Table 2. Our final concern was whether these results remain robust against other model specifications and samples. To examine this, we estimated several more models and found that the results are robust to isolation of separate models by medical specialty of the physician, to exclusion of state and AMA specialty fixed effects, to the inclusion of physicians not working in solo or group practices, to the exclusion of physicians working in metropolitan statistical areas with a population of 1 million or more, and across the adoption of the alternative regression specification of multinomial logit. Estimation of the results on the subsample of accepting “none” against those accepting “some” resulted in logit estimates that were comparable to those of the main model, but were not significant at conventional levels.

7 Simulation This paper has found evidence that increased Medicare payment generosity significantly impacts access to care for Medicare patients by exploiting imperfections in Medicare’s geographic practice cost adjustment mechanism. We now use models (4) and (6) in Table 2 to predict the effects of two potential payment reforms: first, the elimination of all Gw price floors and the 25% deviation restriction, and second, complete neutralization of the fee schedule by paying physicians in accordance with the more accurate G∗w and G∗e indices

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derived in this paper, in conjunction with the elimination of the price floor and 25% deviation restriction. The simulated results under model (4) indicate removing both the price floor and the 25% restriction would result in approximate one percentage point increase in the percentage of physicians accepting all Medicare patients, a two percentage point increase for those in underpaid areas with virtually no change for those served in areas overcompensated by the geographic cost adjustment system. Model (6) indicates an overall impact of a 0.75 percentage point reduction in the percentage of physicians accepting no new Medicare patients, with underpaid physicians seeing a 1.75 percentage point reduction in the probability of not accepting any Medicare beneficiaries. Simulating the complete neutralization of the fee schedule (i.e., when the work and practice expense components fully adjust for regional differences in costs), Models (4) and (6) imply a two-percentage point increase in the percentage of physicians who are accepting all Medicare patients and a 0.1 percentage point reduction in the percentage of physicians accepting no new Medicare patients. Despite net gains, such a policy is not uniformly positive across payment areas, because physicians in overcompensated areas reduce their probability of accepting all new Medicare patients by 0.33 percentage points and increase their probability of accepting no new Medicare patients by 1.49 percentage points. Among those in under-compensated areas, such a policy increases the probability of accepting all new Medicare patients by 5.8 percentage points (under model [4]) and reduces the probability of accepting no new Medicare patients (under Model [6]) by 2.6 percentage points.

8 Conclusions Using data from the 2008 Restricted Health Tracking Physician Survey, this paper finds evidence that geographic payment distortions indeed influence physicians’ willingness to accept new Medicare patients. Specifically, the structure and design of the Medicare Part B payment system is more generous to physicians practicing in lower cost areas and is less generous to physicians serving beneficiaries in areas with higher levels of cost. Physicians appear to respond to these incentives by improving or limiting their acceptance of new Medicare patients based on the degree of generosity or lack there of from Medicare. Simulating the neutralization of the fee schedule through the removal of CMS price floors and variation in payment restrictions, this study finds potential net improvements in physicians willingness to accept Medicare patients, with willingness to accept new patients slightly reduced among those in low cost areas. CMS and Congress should be conscious of their payment formulas impact on access to care. Our analysis has shown that in 2008 the existence of underpayment in more urban/high cost areas discouraged some physicians in those communities from accepting Medicare patients, while the existence of overpayment in lower cost/rural areas encouraged some in those areas to accept such


15

patients. Further research is necessary to determine the welfare ramifications of inaccuracies in the RBRVS payment formula, and whether neutralization of the geographic adjustment factors are warranted.

References Adams K. 1994. The effect of increased Medicaid fees on physician participation and enrollee service utilization in Tennessee. Inquiry, 31: 173-189. Bureau of Labor Statistics, 2008 Occupational Employment and Wage Estimates. http: //www.bls.gov/oes/oes_dl.htm. [Accessed 9.20.2012] Brunt CS. and Jensen GA. 2012. Pricing distortions in Medicare’s physician fee schedule and patient satisfaction with care. Working Paper. Department of Economics, Georgia Southern University. Center for Medicare and Medicaid Services. Medicare Program; Revisions to Payment Policies Under the Physician Fee Schedule, and Other Part B Payment Policies for CY 2008. CMS-1385-FC. Center for Studying Health System Change. Health Tracking Physician Survey, 2008 [United States]. ICPSR27202-v1. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2010-02-16. doi:10.3886/ICPSR27202.v1 Chou WC, Cooney LM, Van Ness PH, Allore HG, and Gill TM. 2007. Access to primary care for Medicare beneficiaries. Journal of the American Geriatric Society, 55(5): 765-768. Coburn AF, Long SH, and Marquis MS. 1999. Effects of changing Medicaid fees on physician participation and enrollee access. Inquiry, 36: 265-279. Cunningham PJ and Nichols LM. 2005. The effects of Medicaid reimbursement on the access to care of Medicaid enrollees: a community perspective. Medical Care Research and Review, 62: 676-696. Cunningham P. 2010. Physician reimbursement and participation in Medicaid. Presentation to the Medicaid and CHIP Payment and Access Commission, Washington, DC, on September 23, 2010. Damiano PC, Momany ET, Willard JC, and Jogerst GJ. 1997. Factors affecting primary care physician participation in Medicare. Medical Care, 35(10): 1008-1019. Decker, SL. 2007. Medicaid physician fees and the quality of medical care of Medicaid patients in the USA. Review of Economics of the Household, 5: 95-112. Gillis KD and Lee DW 1997. Medicare, access, and physicians’ willingness to accept new Medicare patients. Quarterly Review of Economics and Finance, 37(3): 589-603. Gold M, Jacobson G, Damico A, and Neuman T. 2012. Medicare Advantage 2012 Data Spotlight: Enrollment Market Update. Kaiser Family Foundation, Palo Alto, CA. Government Accountability Office. 2007. Medicare Geographic Areas Used to Adjust Physician Payments for Variation in Practice Costs Should be Revised. GAO-07-466. Report to the Chairman, subcommittee on Health, Committee on Ways and Means, U.S. House of Representatives. Hadley J, Reschovsky J, Corey C, and Zuckerman S. 2009/2010. Medicare fees and the volume of physician services. Inquiry, 46: 372-390. Hasnain R, Hibbard JH, and Weeks EC. 1992. Determinants of physician acceptance of assignment: an examination of Medicare beneficiary characteristics. Medical Care, 30(1): 58-66. Kaiser Family Foundation. 2011. Medicare Spending and Financing Fact Sheet. September 2011. Long SH, Settle RF, and Stuart BC. 1986. Reimbursement and access to physicians’ services under Medicaid. Journal of Health Economics, 5: 235-251. McGuire TG and Pauly MV. 1991. Physician response to fee changes with multiple payers. Journal of Health Economics, 10: 385-410. Medicare Payment Advisory Commission. 2011. Report to the Congress: Medicare Payment Policy. Statement of Glenn M. Hackbarth before the Subcommittee on Health, Committee on Ways and Means, U.S. House of Representatives on March 15, 2011.

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Rodgers JF and Musacchio RA. 1983. Physician acceptance of Medicare patients on assignment. Journal of Health Economics, 2: 55-73. Shen YC and Zuckerman S. 2005. The effect of Medicaid payment generosity on access and use among beneficiaries. Health Services Research, 40(3): 723-744. Sloan FA, Mitchell JB, and Cromwell J. 1978. Physician participation in state Medicaid programs. Journal of Human Resources, 13: 211-45. Showalter MH. 1997. Physicians’ cost shifting behavior: Medicaid versus other patients. Contemporary Economic Policy, 15(4): 74-84. U.S. Department of Housing and Urban Development. 2008. 2008 Revised County Level Fair Market Rents. http://www.huduser.org/portal/datasets/fmr/fmr2008f/index.html# 10fmr. [Accessed 9.20.2012]


17

Table 1 Summary of Physician and Practice Characteristics by Dependent Variable Response

Full Sample

Independent Variable

Definition

Gw − G∗ w

Percentage Work RVU overpayment

Ge −

G∗ e

lyrprac

Percentage Practice Expense RVU overpayment log of years in practice

male

1 if Physician Male

physgrt3

1 if physician works in group practice of three or more physicians (group practice of 1 or 2 physicians is reference category) 1 if physician is part owner of main practice 1 if physician is an employee at main practice 1 if independent contractor at main practice 1 if compensated by salary adjusted for performance 1 if compensated hourly or through time based pay 1 if compensated through share of practice billings and workload 1 if compensated as a solo practitioner

partowner employee contractor persalarypay hourlypay workloadpay solopracpay asthma

hisppt

1 if practice routinely treats patients with Asthma 1 if practice routinely treats patients with Diabetes 1 if practice routinely treats patients with Depression 1 if practice routinely treats patients with Chronic Heart Failure 1 if a hospital has an ownership interest in practice percentage of patients that are African American percentage of patients that are Hispanic

asiapt

percentage of patients that are Asian

natvpt

physpcap

percentage of patients that are Native American 1 if whole county is designated as a Health Professional Shortage Area county level physicians per 100 population

popdensity

county population per square mile

northeast

1 if physician resides in northeast

midwest

1 if physician resides in midwest

west

1 if physician resides in west

diabetes depression heartfailure hosownint blckpt

HPSA

None 547 (16.56%)

Some 465 (14.07%)

Most 605 (18.31%)

All 1687 (51.06%)

Mean (sd)

Min. [Max.]

Mean (sd)

Mean (sd)

Mean (sd)

Mean (sd)

4.333 (0.182) -16.326 (0.183) 2.722 (0.012) 0.751 (0.008) 0.548 (0.009)

-30.056 [34.632] -60.626 [10.902] .6931 [3.829] 0 [1] 0 [1]

1.804 (0.506) -18.623 (0.525) 2.736 (0.029) 0.617 (0.021) 0.438 (0.022)

4.706 (0.527) -16.453 (0.55) 2.742 (0.03) 0.737 (0.021) 0.478 (0.024)

4.192 (0.469) -16.806 (0.469) 2.776 (0.027) 0.774 (0.018) 0.533 (0.021)

5.049 (0.277) -15.431 (0.26) 2.694 (0.018) 0.786 (0.01) 0.604 (0.012)

0.328 (0.008) 0.226 (0.007) 0.037 (0.003) 0.481 (0.009) 0.033 (0.003) 0.253 (0.008) 0.083 (0.005) 0.546 (0.007) 0.558 (0.008) 0.554 (0.008) 0.466 (0.008) 0.103 (0.005) 14.039 (0.266) 13.802 (0.289) 5.502 (0.18) 1.825 (0.146) 0.43 (0.008) 0.329 (0.004) 2664.753 (135.804) 0.217 (0.004) 0.222 (0.005) 0.213 (0.004)

0 [1] 0 [1] 0 [1] 0 [1] 0 [1] 0 [1] 0 [1] 0 [1] 0 [1] 0 [1] 0 [1] 0 [1] 0 [100] 0 [100] 0 [100] 0 [100] 0 [1] 0 [2.972] .3 71201 0 1 0 [1] 0 [1]

0.289 (0.02) 0.176 (0.017) 0.038 (0.008) 0.382 (0.021) 0.055 (0.01) 0.276 (0.019) 0.126 (0.015) 0.652 (0.021) 0.477 (0.022) 0.586 (0.022) 0.281 (0.02) 0.063 (0.01) 11.808 (0.635) 15.677 (0.837) 6.501 (0.533) 1.345 (0.316) 0.43 (0.021) 0.33 (0.008) 3032.487 (393.82) 0.215 (0.017) 0.134 (0.014) 0.286 (0.019)

0.34 (0.022) 0.186 (0.018) 0.038 (0.009) 0.46 (0.024) 0.028 (0.008) 0.257 (0.021) 0.116 (0.015) 0.649 (0.022) 0.668 (0.022) 0.73 (0.021) 0.56 (0.023) 0.103 (0.014) 12.629 (0.665) 13.901 (0.852) 5.206 (0.456) 1.03 (0.189) 0.443 (0.023) 0.331 (0.011) 3282.986 (512.475) 0.221 (0.019) 0.159 (0.017) 0.224 (0.019)

0.352 (0.02) 0.217 (0.017) 0.029 (0.007) 0.539 (0.021) 0.014 (0.005) 0.243 (0.018) 0.072 (0.011) 0.548 (0.02) 0.593 (0.02) 0.589 (0.02) 0.505 (0.02) 0.119 (0.013) 13.5 (0.582) 12.563 (0.599) 5.672 (0.43) 2.419 (0.425) 0.433 (0.02) 0.331 (0.008) 2074.28 (247.84) 0.159 (0.015) 0.241 (0.017) 0.242 (0.017)

0.329 (0.012) 0.255 (0.011) 0.039 (0.005) 0.495 (0.012) 0.035 (0.005) 0.249 (0.011) 0.064 (0.006) 0.487 (0.012) 0.541 (0.012) 0.485 (0.012) 0.484 (0.012) 0.109 (0.008) 15.275 (0.41) 13.641 (0.409) 5.22 (0.245) 1.972 (0.212) 0.426 (0.012) 0.328 (0.005) 2595.873 (190.937) 0.237 (0.008) 0.258 (0.009) 0.177 (0.008)

Dependent Variable: Is your practice accepting all, most, some, or no new patients who are insured through Medicare, including Medicare managed care patients? Date are drawn from the 2008 Restricted Health Tracking Physician Survey.

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Table 2 Estimated Coefficients and their Standard Errors (in parentheses) Showing the Effects of Part B Payment Generosity on Physician Acceptance of New Medicare Patients (N=3304) Model

(1)

(2)

(3)

(4)

(5)

(6)

Dependent Variable:

Degree of Acceptance

Accepting All New

Accepting No New

Gw − G∗w

Physician Controls

0.013** (0.006) 0.006 (0.004) no

0.014** (0.006) 0.007 (0.005) yes

0.010 (0.006) 0.008 (0.005) no

-0.021** (0.009) -0.004 (0.007) no

P-value of joint significance Estimation Method

0.0034 0.0053 Ordered Logit

0.0119

Ge − G∗e

0.011 (0.007) 0.009* (0.005) yes

0.0116 Logit

-0.018* (0.010) -0.004 (0.008) yes

0.0259 0.0862 Logit

Notes: * p