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MEDICARE SKILLED NURSING FACILITY REIMBURSEMENT AND UPCODING JOHN R. BOWBLIS a, * and CHRISTOPHER S. BRUNT b a Department of Economics, Farmer School of Business, Miami University, Oxford, OH, USA b Department of Finance and Economics, Georgia Southern University, Statesboro, GA, USA ABSTRACT Post-acute care provided by skilled nursing facilities (SNFs) is reimbursed by Medicare under a prospective payment system using resource utilization groups (RUGs) that adjust payment intensity on the basis of predened ranges of weekly therapy minutes provided and the functionality of the patient. Individual RUGs account for differences in the intensity of care provided, but there exists signicant regional variation in the payments SNFs receive from Medicare due to the use of geographic adjustment factors. This paper is the rst to use this geographic variation in the generosity of Medicare reimbursement to empirically test if SNFs respond to payment differences between RUG categories. The results are highly suggestive that SNFs upcode patients by providing additional therapy minutes to increase revenue, whereas we nd no evidence of upcoding related to patient functionality scores. Simulating how different payment differentials affect RUG selection, we predict that reducing the nancial incentive to upcode could result in signicant savings to Medicare. Copyright © 2013 John Wiley & Sons, Ltd. Received 24 September 2012; Revised 14 April 2013; Accepted 20 May 2013 JEL Classication: I11, I18 KEY WORDS: skilled nursing facilities; upcoding; Medicare; Prospective Payment System; reimbursement 1. INTRODUCTION Regulators are often concerned with creating a reimbursement system that incentivizes rms to provide high quality in an efcient manner. When the services produced by the regulated rms are veriable, complete contracts can be written that use a xed price to achieve these goals (Shleifer, 1984). In the healthcare sector, when prices are xed, the provider retains any prots generated through improved operational efciency, and the reimbursement scheme is considered high powered (Newhouse, 1996, 2003/2). Because medical care is not perfectly veriable, one concern with high-powered reimbursement systems is that they create incentives for providers to engage in activities that provide suboptimal care to patients to increase prots. If providers are self-interested, one mechanism that can be used is stinting on unveriable dimensions of care, such as providing fewer medical tests (Chalkley and Malcolmson, 1998). A second mechanism that can be used when reimbursement rates are above the cost of treatment is to harness the ambiguity in treatment guidelines to overprovide services and increase revenue. Over the last three decades, the Centers for Medicare and Medicaid Services (CMS) has implemented a high-powered reimbursement system to pay for care provided to Medicare beneciaries called the Prospective Payment System (PPS). This high-powered system pays a case-mix adjusted xed reimbursement rate that allows providers to keep any prot they may generate through operational efciency. Whereas there has been *Correspondence to: Miami University, Oxford, OH 45056, USA. E-mail: [email protected] Copyright © 2013 John Wiley & Sons, Ltd. HEALTH ECONOMICS Health Econ. 23: 821840 (2014) Published online 17 June 2013 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/hec.2959

MEDICARE SKILLED NURSING FACILITY REIMBURSEMENT AND UPCODING

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MEDICARE SKILLED NURSING FACILITY REIMBURSEMENTAND UPCODING

JOHN R. BOWBLISa,* and CHRISTOPHER S. BRUNTb

aDepartment of Economics, Farmer School of Business, Miami University, Oxford, OH, USAbDepartment of Finance and Economics, Georgia Southern University, Statesboro, GA, USA

ABSTRACTPost-acute care provided by skilled nursing facilities (SNFs) is reimbursed by Medicare under a prospective payment systemusing resource utilization groups (RUGs) that adjust payment intensity on the basis of predefined ranges of weekly therapyminutes provided and the functionality of the patient. Individual RUGs account for differences in the intensity of careprovided, but there exists significant regional variation in the payments SNFs receive from Medicare due to the use ofgeographic adjustment factors. This paper is the first to use this geographic variation in the generosity of Medicarereimbursement to empirically test if SNFs respond to payment differences between RUG categories. The results are highlysuggestive that SNFs upcode patients by providing additional therapy minutes to increase revenue, whereas we find noevidence of upcoding related to patient functionality scores. Simulating how different payment differentials affect RUGselection, we predict that reducing the financial incentive to upcode could result in significant savings to Medicare.Copyright © 2013 John Wiley & Sons, Ltd.

Received 24 September 2012; Revised 14 April 2013; Accepted 20 May 2013

JEL Classification: I11, I18

KEY WORDS: skilled nursing facilities; upcoding; Medicare; Prospective Payment System; reimbursement

1. INTRODUCTION

Regulators are often concerned with creating a reimbursement system that incentivizes firms to provide highquality in an efficient manner. When the services produced by the regulated firms are verifiable, completecontracts can be written that use a fixed price to achieve these goals (Shleifer, 1984). In the healthcare sector,when prices are fixed, the provider retains any profits generated through improved operational efficiency, andthe reimbursement scheme is considered high powered (Newhouse, 1996, 2003/2). Because medical care is notperfectly verifiable, one concern with high-powered reimbursement systems is that they create incentives forproviders to engage in activities that provide suboptimal care to patients to increase profits. If providers areself-interested, one mechanism that can be used is stinting on unverifiable dimensions of care, such asproviding fewer medical tests (Chalkley and Malcolmson, 1998). A second mechanism that can be used whenreimbursement rates are above the cost of treatment is to harness the ambiguity in treatment guidelines tooverprovide services and increase revenue.

Over the last three decades, the Centers for Medicare and Medicaid Services (CMS) has implemented ahigh-powered reimbursement system to pay for care provided to Medicare beneficiaries called the ProspectivePayment System (PPS). This high-powered system pays a case-mix adjusted fixed reimbursement rate thatallows providers to keep any profit they may generate through operational efficiency. Whereas there has been

*Correspondence to: Miami University, Oxford, OH 45056, USA. E-mail: [email protected]

Copyright © 2013 John Wiley & Sons, Ltd.

HEALTH ECONOMICSHealth Econ. 23: 821–840 (2014)Published online 17 June 2013 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/hec.2959

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significant focus on the use of stinting to increase profits in PPS (for example, Newhouse and Byrne, 1988;Freiman et al., 1989; Ellis and McGuire, 1996), less is known about the overprovision of services to increaserevenues. The few studies that do exist focus on ‘upcoding’. Upcoding is the overprovision of treatmentservices or the tendency to over-report patient severity to place patients into higher-reimbursed codes. Oneearly paper focusing on upcoding found evidence that hospitals increased the use of reimbursement codes thathave the largest price increases after a change in reimbursement rates (Dafny, 2005). Since then, additionalstudies have found evidence of upcoding in Medicare-reimbursed hospitalizations and outpatient physicianvisits (Silverman and Skinner, 2004; Dafny and Dranove, 2009; Brunt, 2011).

The ability to upcode patients in high-powered reimbursement systems is a function of the verifiability of standardcare. The lack of clinical consensus in defining standard care compounds verification problems by increasing theambiguity of ‘appropriate’ treatment. In essence, it blurs the division between heterogeneity in practitioner treatmentdecisions and treatment for the purpose of increasing revenues. In all the services that Medicare pays for, ambiguity isgreatest in the provision of post-acute care (Newhouse, 2003/2). Post-acute care is rehabilitative services provided topatients after hospitalization. The most expensive form of this care occurs in skilled nursing facilities (SNFs). Sincethe implementation of the PPS for SNFs in 1998, spending on SNF post-acute care has been the fastest-growing com-ponent of Medicare expenditure and currently constitutes over 8% of total Medicare spending. In fact, between 1998and 2009, the number of SNF days paid for byMedicare increased over 50%, from 45.4 to 68.4million covered days,but total Medicare payments increased 127.5%, from $11.2bn to $25.5bn (CMS, 2010).

Even with the significant growth in program expenditures, little is known about upcoding by SNFs with theexception of a few government reports. In a series of reports by the Office of Inspector General (OIG, 2006,2012), the OIG argued that SNFs submit claims that are not supported by medical records, resulting in$542m in potential overpayments in fiscal year 2002 and $1.5bn in fiscal year 2009. However, these reportsare based on a small sample of SNF stays, for example, 499 claims representing 245 stays for the report onfiscal year 2009. In another report, the OIG (2010) found, between 2006 and 2008, that SNFs increased theproportion of patients classified into the highest-reimbursement codes. Despite evidence of questionablebehavior, none of these OIG reports study how financial incentives affected code selection. Therefore, there isa need to systematically study how the financial incentives associated with PPS for SNFs can lead to upcoding.

The primary objective of this paper is to provide insight into these financial incentives and determine whichaspects of the reimbursement system SNFs are using to increase revenues without relying on a review ofmedical records. Although there are some studies that have looked at various aspects of PPS in SNFs, mostof this work focuses on how PPS affected quality and ownership structure (Wodchis et al., 2004a, 2004b;Konetzka et al., 2004, 2006; Unruh et al., 2006; Bowblis, 2011). There is limited work that focuses on the treat-ment patients receive (White, 2003;Wodchis, 2004;Wodchis et al., 2004a, 2004b;Murray et al., 2005; Grabowskiet al., 2011), and to our knowledge, there is currently no scholarly work that directly studies how these treatmentscould result in increased revenues through upcoding.

In this paper, we develop a theoretical model that demonstrates that SNFs can be responsive to the financialincentives to upcode. These financial incentives are defined as the difference in payment rates betweenreimbursement codes. A major challenge in empirically determining if SNFs react to the incentive to upcode is theinability to identify a group of SNFs that do not face an incentive to upcode. Therefore, we utilize an identificationstrategy that has been used in the past and exploit the fact that there is geographic variability in the financial incentiveto upcode (Escarce, 1993; Yip, 1998; Hadley et al., 2001; Kaestner and Guardado, 2008). Specifically, we provideempirical evidence that the geographic adjustment factors used to account for the differences in the cost of providingcare across the USA are imperfect. This variability leads to ‘real’ differences in the financial incentive to upcodedepending on the cost of providing care and the formula used by the CMS to determine reimbursement rates.

Exploiting this geographic variability, we model how SNFs react to the financial incentives associated withplacing patients into reimbursement codes using a national, patient-level dataset for Medicare-reimbursed SNFpatients with hip fractures and strokes in the USA in calendar year 2005. Although the CMS implemented a newSNF reimbursement system in 2011, the essential elements that determine reimbursement codes remain unchangedfrom the year we study. We find that after controlling for an extensive set of patient and SNF characteristics, SNFs

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respond to these financial incentives and provide a greater number of therapy minutes to increase revenue,although we find no significant evidence of upcoding solely through over-reporting of patient severity.

The rest of the paper begins by describing howMedicare reimburses SNFs using resource utilization group (RUG)codes and presents a theoretical model that demonstrates how the choice of RUG codes is determined throughdifferences in payments between RUGs. Because the identification strategy requires geographic adjustment toRUG payments to imperfectly relate to costs, Section 3 provides empirical evidence that supports this claim.Section 4 describes the upcoding analysis and results. Section 5 concludes and provides suggestions for policy.

2. MEDICARE SKILLED NURSING FACILITY REIMBURSEMENT AND THEORETICAL MODEL

2.1. Background on skilled nursing facility reimbursement

Medicare reimburses SNFs a fixed amount for each patient day that varies on the basis of each patient’s RUGclassification. Each RUG consists of labor and nonlabor components, which account for differences in theintensity of care provided. The base labor and nonlabor components used for the payment calculation are thesame across the entire country; however, they are dichotomized into two separate payment schedules to reflectwhether an SNF operates in a rural or urban area. The labor component is further geographically adjusted usinga nursing/staffing wage index to account for regional variation in labor costs. In whole, the per diem reimburse-ment Medicare pays for a given RUG category k is given by

Paymentk

Luk �WIMSA þ Cuk ; if urban

Lrk �WIS þ Crk ; if rural

8><>:

(1)

where Lk and Ck represent a particular RUG’s labor and nonlabor components, respectively, and the corre-sponding superscripts of ‘u’ and ‘r’ indicate if the rural or urban payment schedule is used. WI representsthe wage index, which is calculated at the state level for rural SNFs and at the Metropolitan Statistical Area(MSA) level for urban SNFs, denoted by the subscripts ‘S’ and ‘MSA’, respectively.

Although Equation (1) determines the amount an SNF will be paid for a given RUG k, an SNF still needs todetermine a patient’s RUG classification. Under the RUG-III system, which is in effect during the study period,patients receiving rehabilitative care are classified into 14 different RUGs that are broadly categorized as ‘low’,‘medium’, ‘high’, ‘very high’, and ‘ultra high’ on the basis of the number of therapy minutes received by thepatient. Additionally, each SNF is required to calculate the degree of functional limitations for each patientusing the activities of daily living (ADL) index score. The ADL index score, which ranges from 4 to 18,measures the level of patient need in terms of bed mobility, transferring, toilet use, eating, and fluid intake.Table I shows the number of therapy minutes and ADL index scores for each RUG code.

The last column of Table I shows that Medicare per diem rates increase through either increases in the ADLindex score or increases in the number of therapy minutes. The key facet of the reimbursement system is thatthe marginal revenue for an additional therapy minute is 0 except when moving into a higher broad rehabilitativecategory. Because only nodal therapy minutes have positive marginal revenue, this may provide SNFs with afinancial incentive to increase the number of therapy minutes if a patient is close to the cutoff for classification intoa higher-reimbursed RUG. A similar incentive exists if a patient is close to the cutoff for the ADL index score.

To illustrate the financial incentives for upcoding, consider a patient in an urban SNF with a wage indexof 1.00. Assume that the patient has an ADL index score of 15 and should receive 710 therapy minutes overa 5-day period to receive optimal care. If the patient is classified in accordance with their need into the RUGcode RVB, then the SNF will receive $348.21 per day from Medicare (Table I). If the SNF provides an addi-tional 10 therapy minutes, then the patient is coded RUB, which is reimbursed at $420.56 per day, a differenceof $72.35. The cost to the nursing home is only an additional 10 min of therapist time, if actually provided.Instead, assume the patient has an ADL index score that could be recoded to 16 because of ambiguity in

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how to define the needs of the patient. In this scenario, the patient moves from RUG code RVB to RVC, and thenursing home receives an additional $12 per day. Finally, one could imagine a scenario in which the patientreceives both the extra therapy minutes and an ADL index score recoded up to 16. The patient is now in thehighest-reimbursed code (RUC) at $467.21 per day, a difference of $119.00. Therefore, there are significantfinancial incentives to upcode patients by either providing additional therapy minutes or, if there is ambiguityin the level of need, allowing the SNF to report higher ADL index scores.

Because the ADL index score and number of therapy minutes are determined by the SNF and are self-reported to the CMS for reimbursement, it is a logical argument that SNFs should code all patients into thehighest-reimbursed codes. This does not occur because CMS auditors and subcontractors are permitted toconduct random and target medical reviews of claims for the purposes of detecting improper payments, fraud,and abuse. In the case of ADL index scores, SNFs are required to report ADL index scores on three differentforms (Form UB-04, case-mix documentation, and the Minimum Data Set (MDS)). Any lack of congruenceacross these forms can increase the risk of audit. In the case of therapy minutes, prior to the implementationof PPS, there existed some distribution of therapy minutes below 720 min. Therefore, any SNF that providesa significantly larger proportion of patients in higher-reimbursed RUG codes compared with peer groups couldincrease the risk of an audit. By using this framework, the financial incentive to upcode can be illustrated in atheoretical model adapted from an earlier work by Brunt (2011).

2.2. Theoretical model

For simplicity, the model assumes that there are only two RUGs, RUG1 and RUG2, that require a documentedcase-mix factor (i.e., number of rehabilitative minutes and ADL index) at a minimum of t1 and t2, respectively.SNFs maximize the daily profit per representative patient and face a daily per-patient fixed cost of C0 andvariable cost C(t), with C(t) and C0> 0. The payments of RUG1 and RUG2 are p1 and p2, respectively, withp2> p1 and t2> t1. Let t* represent the true case-mix factor. For example, t* might represent the cost-effectiveamount of therapy minutes. If t*< t2, then the SNF could classify the patient into RUG1 and will receivepayment p1. In this situation, an SNF knows it will receive the following profit with certainty:

p1 ¼ p1 � c0 � c tð Þ if t1 < t < t2:

The SNF has an opportunity to upcode the patient by either using more therapy minutes or overstating thenumber of ADLs to get the patient classified into RUG2. However, there is a chance that the SNF will be caught

Table I. Rehabilitation RUG-III categories

Category Therapy minutes ADL index score RUG code Reimbursement rate ($)

Ultra high ≤720 16–18 RUC 467.219–15 RUB 420.564–8 RUA 397.90

Very high ≤500 16–18 RVC 360.219–15 RVB 348.214–8 RVA 317.55

High ≤325 13–18 RHC 330.368–12 RHB 303.704–7 RHA 278.37

Medium ≤150 15–18 RMC 325.288–14 RMB 290.634–7 RMA 273.30

Low ≤45 14–18 RLB 259.154–13 RLA 217.83

The number of therapy minutes reflect the minimum number of therapy minutes required in a 5-day period. The Federal reimbursement ratereflects the total payment, as per Equation (1), for an urban skilled nursing facility in a geographic area with a wage index of 1.00 in 2005.Reported reimbursement rates do not reflect the 6.7% increase applied for 2005 under Section 314 of the Benefit Improvement Act.ADL, activities of daily living; RUG, resource utilization group.

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by the CMS and would face a penalty C with probability 1� g. This penalty could be a reduction in payment, adenial of claim, or a severe fine. If the SNF chooses to upcode, the profit received is

p2 ¼ p2 � c0 � c tð Þ if t > t2 with probability g

and

p3 ¼ p2 � c0 � c tð Þ � C if t > t2 with probability 1� g:

The expected utility of upcoding is

gð Þu p2ð Þ þ 1� gð Þu p3ð Þ:The SNF will choose to upcode if (g)u(p2) + (1� g)u(p3)�U(p1)> 0 and, if this equation is totally differ-

entiated, yields

@gu p2ð Þ þ gu0p2ð Þ @p2 � @c0 � ct@t½ � � @gu p3ð Þ

þ 1� gð Þu0p3ð Þ @p2 � @c0 � ct@t� @C½ � � U

0p1ð Þ @p1 � @c0 � ct@t½ �: (2)

Equation 2 is increasing in g and p2, decreasing in C and p1, and ambiguous in t. Equation 2 implies that anincrease in the payment differential between RUG1 and RUG2 (p2� p1) will increase the likelihood that apatient is classified into the higher-reimbursed RUG, in this case, RUG2. This result is a function of the absolutedifference between p2 and p1 and is indifferent to the circumstances of the change (i.e., if p1 or p2 is thepayment that changes).

It can easily be shown that if the number of RUG groups increases, the RUG chosen will be a function of thepayment differentials between the RUG associated with the true case-mix factor-appropriate RUG and thehigher-reimbursed and lower-reimbursed RUGs. Thus, if there are three RUGs and RUG2 is the true RUG,the incentive to upcode is a function of the difference in payment between RUG1 and RUG2, called the lowerpayment differential, and RUG2 and RUG3, called the upper payment differential.

For illustration, assume that an SNF classifies a patient into RUG code RVB. If the SNF downcodes the patientand provides fewer therapy minutes, then the patient is now in RUG code RHB. The difference in paymentbetween RHB and RVB is the lower payment differential. Conversely, if the SNF upcodes the patient and providesmore therapy minutes, the patient would be in RUG code RUB. The upper payment differential is the difference inpayments between RVB and RUB. With a representative SNF from Table I, this example has lower and upperpayment differentials of $44.51 and $72.35 in absolute value, respectively. These payment differentials can becalculated for upcoding using only therapy minutes, only ADL index scores, or across both dimensions (Table II).When there is no higher or lower RUG code, the payment differential is assumed to be 0.

The static model presented provides insights into SNF incentives and behavior in the short run, but if thismodel is repeated multiple times, it is expected that upcoding will influence the probability of being caughtin the future. This probability is a function of the verifiability of each element of the payment mechanism. Withno clear treatment guidelines in relation to the appropriate magnitude of therapy minutes, SNFs have sufficientlatitude and discretion in therapy minute selection (OIG, 2010). However, historical norms for initial ADLassessments and randomized SNF record audits, along with the potential for ADL verification in conjunctionwith high-frequency patient readmissions to different facilities, make ADL upcoding a much more riskyproposition. Thus, we would expect a much greater magnitude of upcoding on the ambiguous therapy minutecomponents and a lower degree of upcoding in conjunction with ADL index scores.

To empirically estimate the incentives to upcode, there must be a variation in the payment differentialsacross geographic areas. This variation is easy to demonstrate. For example, an urban SNF with a wage indexof 1.00 will have a payment differential of $72.35 between RUG codes RVB and RUB, but these paymentdifferentials are $61.32 and $83.38 if the wage index is 0.80 and 1.20, respectively. Although there is ageographic variation in payment differentials, theoretically these differences reflect the cost of providing carein various geographic regions, and once the cost of care is accounted for, there is no real variation in fee

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differentials. We argue that although the wage index and rural/urban adjustment factors attempts to adjust forgeographic cost differences, these factors are imperfect. That is, the adjustment factors used by the CMS areimperfect, and geographic variation in payment differentials provides varying incentives to upcode. In the nextsection, we provide empirical evidence that supports this claim.

3. EVIDENCE OF IMPERFECT GEOGRAPHIC ADJUSTMENT FACTORS

In this section, we present empirical evidence that supports the claim that geographic adjustment factors areimperfect, providing differing incentives for SNFs to upcode. We test this assertion by determining if theseadjustment factors are correlated with SNF profit margins. Ignoring differences in efficiency across SNFs, profitmargins should not be correlated with geographic adjustment factors, so long as these factors perfectly adjustfor cost differences. However, if these factors are correlated with profit margins, then the payment differentialsreflect real differences and imply that the incentive to upcode varies across geographic areas.

The key to this test is properly adjusting for the efficiency differences between geographic areas. Forexample, even if Medicare revenues are standardized, profit margins could still be different across SNFs if theefficiency of SNFs varies by geographic region. Following the literature on variation in medical spending, thereis no reason to believe that inefficiency is likely to be based on geography once differences in state regulatory struc-ture, facility characteristics, and patient case mix are taken into consideration (Zuckerman et al., 2010). Therefore,profit margins are regressed on geographic adjustment factors and facility characteristics that may impact costs, aswell as state indicator variables that account for differences in the regulatory structure of each state.

The primary dataset in this analysis is the Medicare Cost Reports for SNFs. The cost reports were created toprovide information on SNFs to determine how to adjust RUG payments. The Medicare Cost Reports containdata on the number of patient days billed for each RUG and the average per diem cost for freestanding SNFs.The cost reports used in the analysis include all reports with a fiscal year start date on or after January 1, 2005,and fiscal year end data on or before December 31, 2005, with a fiscal year of 360–365 days, with a positivenumber of Medicare patient days, and with per diem cost data. We focus only on SNFs that have their entirefiscal year in 2005 because the wage index and base payment rates for the labor and nonlabor componentscan change annually. The Medicare Cost Reports are merged with data on facility structure, case mix, and

Table II. Definition of payment differential variables

Variable DefinitionInterpretation of positive

coefficient estimate

ADL lower payment differential The difference in payments (in absolute value) between the currentlyselected RUG and downcoding the patient to a lower-reimbursedRUG by decreasing the ADL index score

Upcoding

ADL upper payment differential The difference in payments (in absolute value) between the currentlyselected RUG and upcoding the patient to a higher-reimbursedRUG by increasing the ADL index score

Downcoding

Therapy minute lower paymentdifferential

The difference in payments (in absolute value) between the currentlyselected RUG and downcoding the patient to a lower-reimbursedRUG by decreasing the number of therapy minutes

Upcoding

Therapy minute upper paymentdifferential

The difference in payments (in absolute value) between the currentlyselected RUG and upcoding the patient to a higher-reimbursedRUG by increasing the number of therapy minutes

Downcoding

ADL and therapy minute lowerpayment differential

The difference in payments (in absolute value) between the currentlyselected RUG and downcoding the patient to a lower-reimbursed RUGby decreasing the ADL index score and the number of therapy minutes

Upcoding

ADL and therapy minute upperpayment differential

The difference in payments (in absolute value) between the currentlyselected RUG and upcoding the patient to a higher-reimbursed RUG byincreasing the ADL index score and the number of therapy minutes

Downcoding

ADL, activities of daily living; RUG, resource utilization group.

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staffing levels from the Online Survey Certification and Reporting (OSCAR) system, which is a uniform data-base of state nursing home regulatory reviews of all CMS-certified nursing homes, including SNFs. Thesedatasets are merged with data on the geographic adjustment factors for calendar year 2005 taken from theCMS (2004) update (69 FR 45775).

Given that the objective of our analysis is to determine if there exists systematic imperfections in the geo-graphic adjustment of RUG payments and SNF financial incentives, we construct a measure of profit marginbased on the average RUG per diem payment. This average payment is calculated using the RUG paymentand number of patient days. That is, the number of patients for each RUG is multiplied by the RUG paymentto obtain the revenue the facility received for each RUG. Next, the revenue across each RUG is summed toobtain total payments. Finally, the average per diem payment is calculated as total payments divided by totalnumber of Medicare days. Profit margin is defined as the logged difference in the average per diem paymentand per diem cost. One concern is some SNFs report improbably high or negative profit margins. To accountfor potential data errors, the data are restricted to only observations with profit margins within the 1st and 99thpercentiles, leading to a sample of 9124 SNFs.

As per Equation (1), Medicare adjusts SNF payments using geographic adjustment factors that alter RUGpayment on the basis of the rural/urban location and using a wage index that adjusts the payment’s laborcomponent for prevailing wages in the area. To test for imperfections in geographic adjustment factors, weuse ordinary least squares with robust standard errors clustered by state. Each SNF is used as the unit of obser-vation with the SNF’s profit margin (M) as the dependent variable. The resulting empirical model is

M ¼ aþ b1WI þ b2Rþ b3WI � Rþ Xdþ E;

where WI denotes the wage index, R denotes the binary rural status of the SNF, and the vector X representsfacility and state indicator control variables. An interaction of the wage index and the rural indicator variableis also included to capture the potential differences in the effect of the wage index for rural SNFs. The betaparameters identify if the geographic payment factors are correlated with the profit margin. If geographicadjustment factors are perfectly determined, then the coefficient estimates of the betas should not be statisticallydifferent from 0. However, if any of the betas are found to be statistically significant, it suggests that profitmargins are imperfect and different geographic areas have varying financial incentives to upcode.

To control for potential efficiency differences, X includes a set of facility characteristic variables and stateindicator variables. The facility characteristics can be broken down into facility structure, patient payer mix,patient case mix, and facility staffing levels. Facility structure could affect profit margins through differencesin objectives of ownership (e.g., for profit, not for profit, or government), economies of scale (through the sizeof facility), managerial efficiency associated with being part of a multifacility organization, and occupancyrates as a measure of operating efficiency (Sloan et al., 2003).

Payer mix and case mix are included to capture the average difference in patient characteristics across SNFs.Payer mix is defined as the percentage of residents paying through Medicare, Medicaid, and other sources.Because Medicaid reimbursement is the least generous of all payment sources, facilities with a greaterproportion of Medicaid residents may need to be more efficient to operate profitably. However, the ability ofthe SNF to control costs is constrained by the case mix of the residents in the SNF. Although the payment ratesare adjusted for the case mix, there can be some variation in the case mix within a RUG that is not captured bythe payment. Therefore, the average facility acuity level, measured by the Acuindex, and the percentage ofresidents with dementia are included as controls.

Finally, the primary cost of care is staffing, and a facility may voluntarily choose to employ more staff.Because staffing levels can impact per diem costs, the level of staffing for registered nurses, licensed practicalnurses, and certified nurse aides is controlled for in the regression analysis. These staffing variables aremeasured in terms of hours per resident day. When nurse staffing variables are used in the empirical analysis,occasional improbable staffing levels are identified and controlled for using the same method as described byBowblis (2011). That is, for each staff type, an indicator variable for an improbable staffing value is created,and the staffing level for that staff type is coded as 0. These indicator variables are included in the regression.

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The first three columns of Table III reports the summary statistics of the sample by rural/urban status, andthe other columns report the regression results for the key explanatory variables. Overall, SNFs have an averageprofit margin of 1.1%. This is in line with other studies that looked at profit margins (Weech-Maldonado et al.,2012). Nearly four out every 10 SNFs operate in a rural Medicare reimbursement area, and the average facilityhas a wage index of 0.964 with a range of 0.729–1.522. Profit margins are similar between rural and urbanSNFs, but the wage index is higher for urban SNFs. Furthermore, there is more variation in the wage indexfor urban SNFs than for rural SNFs.

The coefficient estimates reported in Table III are robust against the inclusion of a variety of controls. SNFsin rural areas have lower profit margins compared with urban SNFs. A higher wage index is also found to beassociated with higher profit margins, with the effect being larger for rural SNFs. For urban and rural SNFs, aone-standard-deviation change in the wage index results in higher profit margins of 4.2 and 7.6 percentagepoints, respectively. Additionally, the finding that geographic adjustment factors are correlated with profitmargins is robust against alternative specifications of profit margin and supports our claim that geographicadjustment factors are not perfect and that some areas may have a greater incentive to upcode. Although theseresults support our claim, we should note that there is the possibility for some unmeasured patient heterogene-ity, especially between urban and rural SNFs, that may explain the large negative effect of being located in arural area. This implies caution is warranted when interpreting the results beyond the intended claim thatgeographic adjustment factors are imperfect.

4. UPCODING ANALYSIS

4.1. Model specification and data

When an SNF determines reimbursement codes, each patient i is classified into a RUG code k on the basis ofthe number of therapy minutes provided and the ADL index score of the patient. If there is no financial incen-tive to upcode, the SNF will classify patients into RUGs solely on the basis of the medical status of the patient.However, if there is upcoding, as illustrated in the theoretical model, the payment differentials between RUGcodes would affect the probability of being classified into a particular RUG. Empirically, we model the SNFdecision for each patient’s RUG selection using a conditional logit model, also known by some as mixed logitgiven that it contains RUG choice alternatives and alternative-invariant variables (Maddala, 1986; Cameronand Trivedi, 2005; Long and Freese, 2006). To accommodate this econometric specification, each patient-levelobservation is transformed into 12 alternatives, where each alternative represents a potential coding decision ofthe SNF. From these alternatives, RUG payment differentials are calculated. Given that patient-specific andSNF-specific variables are RUG alternative invariant, alternative RUG-specific binaries are interacted withthe alternative-invariant variables to create patient-specific and SNF-specific controls. Formally, we modelthe SNF’s RUG choice using a conditional logit model for each RUG k and patient i,

Pr yi ¼ k xi; zijð Þ ¼ exp zikaþ xibkð ÞXJ

j¼1exp zijaþ xibj

� � for k ¼ 1 to J

with b1 ¼ 0;

(3)

where zik contains the RUG alternative-specific variables, a contains the coefficients for the effects on RUG al-ternative k relative to the base alternative, xi contains RUG-invariant patient and SNF characteristics for patienti, and bk contains the coefficients for the patient characteristic effects.

The RUG alternative-specific variables in each model include the lower and upper payment differentialsassociated with RUG code k and the actual payment received if RUG code k is chosen. The payment differen-tials capture the financial incentives SNFs have to classify patients into various RUGs and are the key variablesin Equation (3). The upper payment differential is the difference between the payments received by the SNF for

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Table

III.

Profitmarginandgeographic

paym

entadjustmentfactors

Sum

marystatistics

Percent

profi

tmargin(0–1)

All

Urban

Rural

Mean(SD)

Mean(SD)

Mean(SD)

Coefficient(SE)

Coefficient(SE)

Coefficient(SE)

Rural

facilityadjustment

0.380(0.485)

——

�0.177*(0.103)

�0.187*(0.107)

�0.167

(0.108)

Wageindex

0.964(0.143)

1.016(0.138)

0.879(0.106)

0.302***

(0.046)

0.291***

(0.048)

0.295***

(0.052)

Wageindex*ruralfacilityadjustment

0.334(0.432)

——

0.251**(0.119)

0.258**(0.124)

0.232*

(0.126)

Profitmargin

0.011(0.247)

0.012(0.253)

0.010(0.010)

——

—Regressionincludes

State

indicatorvariables

XX

XFacility

characteristic

variables

XX

Staffing

variables

XObservatio

ns9124

5660

3464

9124

9124

9124

R2

0.354

0.449

0.467

Standarderrors

areadjusted

forclustering

atthestatelevel.Facility

characteristic

variablesincludeow

nership,

numberof

beds,partof

multifacility

chain,

payermix,occupancyrate,

acuity

level,percentd

ementia,and

theproportio

nof

days

atthedefaultp

aymentrate.Staffing

variablesincluderegistered

nurse,licensedpracticalnurse,certified

nurseaide,occupational

therapy,

physical

therapystaff,andindicatorvariablesforim

probable

staffing

levels.

SD,standarddeviation;

SE,standard

error.

***p

<0.01,*

*p<0.05,*

p<0.1.

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choosing the current RUG code k and upcoding the patient into a higher-reimbursed RUG. Conversely, the lowerpayment differential is the difference in payments from the currently chosen RUG code k and downcoding thepatient into a lower-reimbursed RUG. These changes can occur by changing either the number of therapy minutes,ADL index score, or both. For ease of interpretation, payment differentials are included in the model as absolutevalues. Complete descriptions of payment differentials used in each model are provided in Table II.

Under the null hypothesis that SNFs do not respond to financial incentives, a patient would be classified intoa RUG solely on the basis of alternative-invariant covariates xi that describe the patient, their clinical charac-teristics, and the characteristics of the SNF. This implies that the coefficient estimates for the lower and upperpayment differentials would not be statistically different from 0. However, if the payment differentials arefound to be statistically significant, this is consistent with SNFs responding to financial incentives. If the upperpayment differential is larger, there is a greater financial incentive to upcode a patient. This implies that thecurrent RUG is less likely to be chosen by the SNF because the patient has already been upcoded. Further, ifthe lower payment differential is larger, then this is associated with a financial incentive to upcode. However, largerabsolute values for the lower payment differentials should be associated with a higher probability of choosing thecurrent RUG because the patient has already been upcoded. This implies that positive coefficient estimates for thelower payment differential and negative coefficient estimates for the upper payment differential are consistent withupcoding. If the opposite is found, then larger financial incentives are associated with downcoding.

In this analysis, the primary source of data is theMDS. TheMDS is a federally mandated assessment of all nurs-ing home residents and includes information on demographics, physical and cognitive functioning, diagnoses,treatment received, and the specific RUG classification for each Medicare SNF patient (Hawes et al., 1995).Medicare SNF admission assessments in calendar year 2005 for all SNFs (freestanding and hospital based) aremerged with OSCAR to obtain facility characteristics. The payment rates and payment differentials for eachRUG are calculated using information from the CMS (2004) update (69 FR 45775) for calendar year 2005. Oneconcern with using all Medicare SNF admission assessments is that patients can be admitted for a variety of med-ical conditions. Including patients with different medical conditions can lead to confounding between the incentiveto upcode and patient heterogeneity from clinical factors associated with each medical condition. To control forthis heterogeneity, we restricted the sample to two groups, patients that have a rehabilitative SNF visit for a hipfracture and patients that have a rehabilitative SNF visit for a stroke. Given the infrequency of use of the low re-habilitative RUG category, these patients have been excluded from the analysis (approximately 200 patients). Thisresults in a sample of 95,975 SNF patients with hip fractures and 147,858 patients with strokes.

These data sources provide a wealth of information about the characteristics of the SNF and patient. TheRUG alternative-invariant covariates xi come from these sources and are included in the model to capturepatient and SNF characteristics that can influence which RUG a patient is classified into. Summary statisticsfor these covariates are reported in Table IV. Patient characteristics include gender, age, race, education,short-term and long-term memory loss, cognitive impairment, and indicators for the following medical condi-tions: diabetes, heart disease, cardiac dysrhythmia, heart failure, chronic obstructive pulmonary disease,dementia, anxiety, and depression. SNF characteristics are obtained from OSCAR and include the facility struc-ture, patient payer mix and case mix, and nurse staffing variables used in the profit margin analysis. In addition,regional indicator variables of New England, Mid-Atlantic, Midwest, South, Mountain, and Western states areincluded. Although not reported in our results section, many of these covariates are statistically significant. Thissuggests that upcoding varies with the characteristics of the SNF, independent of financial incentives, but thesefactors are not explored in this paper.

4.2. Results

Figures 1 and 2 report the distribution across therapy minutes for rehabilitative RUG-classified patients that hada hip fracture or stroke and are treated at SNFs in the first and fourth quartiles of the wage index. As can be seenin the figures, there are large increases in the proportion of patients at certain therapy minutes. In particular,there are spikes in the proportion of residents that receive a certain number of therapy minutes at or slightly

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above the minimum number of minutes required to classify a patient into a higher-reimbursed RUG (i.e., 150,325, 500, and 720). This pattern is similar to the one found by Wodchis (2004) for Michigan and Ohio SNFpatients in 1998–1999. This pattern is consistent with upcoding patients by increasing therapy minutes untilthe patient is in a higher-reimbursed RUG. There is also a chance that the patient would have been providedmore therapy minutes to receive optimal care and the facility is withholding care because providing more ther-apy minutes does not increase revenue. Either way, both are consistent with SNFs providing therapy minutes ina manner to increase revenues. Unlike the finding of Wodchis (2004), Figures 1 and 2 show that there is a

Table IV. Summary statistics for samples in upcoding analysis

Hip fracture (1) Stroke sample (2)

ADL lower payment differential 24.154 (10.433) 21.840 (11.921)ADL upper payment differential 14.235 (16.591) 15.989 (17.363)Therapy minute lower payment differential 34.145 (31.138) 36.886 (34.231)Therapy minute upper payment differential 48.727 (35.981) 44.449 (36.796)ADL and therapy minute lower payment differential 56.713 (35.084) 55.362 (39.441)ADL and therapy minute upper payment differential 45.088 (56.163) 41.000 (53.026)Payment 371.022 (60.474) 374.018 (65.156)

Patient demographicsGender 0.758 (0.428) 0.604 (0.489)Age 82.860 (8.648) 79.521 (9.645)Race/ethnicity: Black 0.037 (0.189) 0.131 (0.338)Race/ethnicity: Hispanic 0.023 (0.150) 0.035 (0.183)Race/ethnicity: other non-Caucasian 0.016 (0.126) 0.022 (0.146)Education: less than high school 0.259 (0.438) 0.298 (0.457)Education: more than high school 0.247 (0.431) 0.220 (0.414)Education: missing 0.018 (0.132) 0.020 (0.142)

Patient medical statusShort-term memory problems 0.483 (0.500) 0.598 (0.490)Long-term memory problems 0.246 (0.431) 0.332 (0.471)Cognitive impairment: moderately independent 0.252 (0.434) 0.280 (0.449)Cognitive impairment: moderately impaired 0.252 (0.434) 0.325 (0.469)Cognitive impairment: severely impaired 0.047 (0.211) 0.078 (0.269)Diabetes 0.223 (0.416) 0.369 (0.483)Arteriosclerotic heart disease 0.125 (0.331) 0.157 (0.364)Cardiac dysrhythmias 0.178 (0.382) 0.225 (0.418)Heart failure 0.172 (0.378) 0.241 (0.428)Osteoporosis 0.268 (0.443) 0.122 (0.327)Dementia 0.275 (0.446) 0.286 (0.452)Anxiety 0.110 (0.313) 0.096 (0.295)Depression 0.260 (0.439) 0.299 (0.458)Chronic obstructive pulmonary disease 0.172 (0.378) 0.183 (0.386)

SNF characteristicsNot-for-profit ownership 0.313 (0.464) 0.262 (0.440)Government ownership 0.034 (0.182) 0.031 (0.175)Number of beds 133.535 (79.566) 138.813 (80.916)Chain membership 0.556 (0.497) 0.582 (0.493)Hospital-based SNF 0.104 (0.305) 0.076 (0.265)% residents Medicaid 49.544 (25.163) 54.518 (23.875)% residents Medicare 25.549 (22.659) 22.766 (20.059)Occupancy rates 84.565 (16.601) 85.236 (15.477)Staffing levels: registered nurses 0.494 (0.608) 0.432 (0.535)Staffing levels: licensed practical nurses 0.841 (0.492) 0.823 (0.449)Staffing levels: certified nurse aides 2.208 (0.775) 2.175 (0.745)Staffing levels: occupational therapy 0.179 (0.195) 0.159 (0.173)Staffing levels: physical therapy 0.253 (0.280) 0.219 (0.240)

Patients 95,975 147,858

The table reports the mean and standard deviation (in parentheses) for each variable. For the payment and payment differential variables, thesummary statistics are calculated on the basis of the actual RUG code used for the patient. Summary statistics for inconsistent staffing levelsand region are not reported. The staffing level variables are measured in terms of hours per resident day.ADL, activities of daily living; SNF, skilled nursing facility.

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different pattern in the propensity to upcode on the basis of the wage index of the SNF. In particular, SNFs inthe highest-wage-index areas (fourth quartile) compared with lowest-wage-index areas (lowest quartile) have alarger proportion of residents receiving more therapy minutes around the higher-reimbursed nodal cutoffs. Thiswould suggest that all SNFs may be upcoding using therapy minutes, but the upcoding is more pronounced thehigher the wage index.

Upcoding may also occur through ADL index scores. Figures 3 and 4 report RUG-classified hip fracture andstroke patient ADL index scores for patients in the first and fourth quartiles of the wage index, respectively. Itshould be noted that it is harder to analyze ADL index scores because the cutoffs are different depending onthe number of therapy minutes a patient receives (Table I). There are some distinctions in the pattern ofADL index scores for low-wage-index and high-wage-index SNFs. In particular, there is a greater proportionof high-wage-index SNF patients in the medium ADL range (12–16 ADL score), whereas low-wage-indexSNF patients appear to be more concentrated at the low ADL range (4–8 ADL score) and highest ADL range(16–18 ADL score). Although these differences do not directly suggest upcoding, they do indicate that SNFs

0.0

5.1

.15

Per

cent

age

of S

NF

Pat

ient

s

45 150 325 500 720Therapy Minutes

Low Wage Index High Wage Index

Figure 1. Histogram of therapy minutes for hip fracture patients. SNF, skilled nursing facility

0.0

5.1

Per

cent

age

of S

NF

Pat

ient

s

45 150 325 500 720Therapy Minutes

Low Wage Index High Wage Index

Figure 2. Histogram of therapy minutes for stroke patients. SNF, skilled nursing facility

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respond to financial incentives. Further, the histograms also confirm the importance of evaluating SNF financialincentives regarding RUG choice in terms of both ADL index scores and therapy minutes. This implies thatstudying therapy minutes or ADL upcoding should not be carried out unilaterally because both dimensionsare part of the menu used in the selection of RUG codes.

Equation (3) is estimated for the hip fracture and stroke samples to determine if SNFs upcode therapyminutes and ADL index scores simultaneously (Table V). The analysis includes three sets of lower and upperpayment differentials. The first set is ADL payment differentials. These reflect the payment differences betweenRUGs if the patient is upcoded using only the ADL index score. The second set is therapy minute paymentdifferentials and reflect the unilateral change in payment across RUGs if patients are upcoded using onlytherapy minutes. The final set is ADL and therapy minute payment differentials. These payment differentialsassume that upcoding in ADL index scores and therapy minutes occurs simultaneously.

In Table V, the first column for each sample only allows upcoding in ADLs or therapy minutes unilaterally,whereas the second column also allows for upcoding in both dimensions simultaneously. Regardless ofwhether the joint ADL and therapy minute payment differential is included in the regression, the coefficient

0.0

5.1

.15

.2

Per

cent

age

of S

NF

Pat

ient

s

4 8 13 14 15 16 18ADLs

Low Wage Index High Wage Index

Figure 3. Histogram of activities of daily living (ADLs) for hip fracture patients. SNF, skilled nursing facility

0.0

5.1

.15

.2

Per

cent

age

of S

NF

Pat

ient

s

4 8 13 14 15 16 18

ADLs

Low Wage Index High Wage Index

Figure 4. Histogram of activities of daily living (ADLs) for stroke patients. SNF, skilled nursing facility

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estimates for the other payment differentials are similar. The ADL lower payment differential is positive andstatistically significant and is consistent with SNFs having a higher probability of choosing the higher-reimbursed RUG group. The effect of the ADL upper payment differential is positive, but none of thecoefficient estimates are statistically significant. Both coefficient estimates for the unilateral lower and upperpayment differentials based on therapy minutes are statistically significant, with signs consistent with upcoding.The signs of the coefficient estimates for the joint ADL and therapyminute payment differential are consistent withupcoding as well. The lower payment differential is statistically significant in both the hip fracture and strokesamples, whereas the upper payment differential is only marginally significant in the stroke sample. These regres-sion results are suggestive of upcoding through therapy minutes and jointly with therapy minutes and ADL indexscores. However, the statistically insignificant but positive values for the coefficient estimates of the ADL upperdifferential make it difficult to determine if there is upcoding through ADL index scores alone.

To quantify the effects of these differentials, we compare the distribution of RUG choices under differentfinancial incentives. Specifically, Table VI estimates the distribution of RUG choices for different wage indicesand rural/urban combinations. The reported distributions are calculated using the coefficient estimates thatallow for upcoding in both directions (i.e., columns 2 and 4 of Table V) by applying the wage differentialsassociated with a different wage index, while maintaining each RUG’s payment level and patient/SNF charac-teristics. For example, all SNFs have payment rates associated with a wage index of 1.00, but the paymentdifferentials associated with a wage index of 0.75 are applied. By comparing the distributions, we are able todetermine how the distribution of RUG choices change as the financial incentives to upcode become smaller.

To determine if there is upcoding in therapy minutes, one can look at the change in the percentage ofresidents in each broad category across varying payment differential wage indices. Consistent with upcoding,the percentage of people in the higher-reimbursed categories of very high and ultra high declines as the wageindex used to determine the payment differentials decreases in Table VI. For example, in the first row of the hipfracture sample, all SNFs are treated as urban with wage indices of 1.00. In this scenario, 7.9%, 36.9%, 37.7%,and 17.4% of patients are in the medium, high, very high, and ultra high categories, respectively. If the paymentdifferentials are reduced to the levels associated with a wage index of 0.75, the corresponding percentages are11.8%, 46.0%, 36.0%, and 6.3%, respectively. This supports the hypothesis that higher wage differentials areassociated with upcoding in terms of therapy minutes as there is a decrease in the proportion of patients in the

Table V. Regression results for therapy minute and ADL joint code selection decisions

Hip fracture sample Stroke sample

1 2 3 4

ADL lower payment differential 0.034*** (0.010) 0.033*** (0.012) 0.033*** (0.006) 0.034*** (0.008)ADL upper payment differential 0.013 (0.010) 0.004 (0.012) 0.008 (0.008) 0.001 (0.009)Therapy minutes lower payment differential 0.053*** (0.014) 0.069*** (0.015) 0.041*** (0.014) 0.055*** (0.010)Therapy minutes upper payment differential �0.010*** (0.003) �0.010*** (0.003) �0.014*** (0.003) �0.014*** (0.003)ADL and therapy minutes lower paymentdifferential

0.028*** (0.010) 0.019*** (0.006)

ADL and therapy minutes upper paymentdifferential

�0.010 (0.006) �0.008* (0.004)

Number of observations 1,151,700 1,151,700 1,774,296 1,774,296Number of patients 95,975 95,975 147,858 147,858

Standard errors adjusted for clustering at the state level are reported in parentheses. All regressions are estimated using a mixed logit modeland include a payment level control, as well as patient and skilled nursing facility (SNF) characteristics. Patient characteristics includegender, age, race, education, short-term and long-term memory loss, cognitive impairment, and indicators for the following medical conditions:diabetes, heart disease, cardiac dysrhythmia, heart failure, chronic obstructive pulmonary disease, dementia, anxiety, and depression. SNFcharacteristic variables include ownership, number of beds, beds squared, part of a multifacility chain, payer mix, occupancy rate, acuity level,percent dementia, and nurse staffing levels. In addition, regional indicator variables of New England, Mid-Atlantic, Midwest, South, Mountain,and Western states are included.ADL, activities of daily living.***p< 0.01, **p< 0.05, *p< 0.1.

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Table

VI.

Predicted

andsimulated

distributio

nsof

resource

utilizatio

ngroupchoice

Base

rural

Payment

wagerate

Paymentdifferential

wagerate

Resourceutilizatio

ngroupcategories

(%)

RMA

RMB

RMC

RHA

RHB

RHC

RVA

RVB

RVC

RUA

RUB

RUC

Panel

A:hipfracture

sample

Urban

1.00

1.00

0.41

3.61

3.90

1.37

9.56

25.98

3.17

25.15

9.41

1.32

11.14

4.98

Urban

1.00

0.75

0.70

5.74

5.32

2.66

12.03

31.27

4.75

21.23

10.05

0.86

4.10

1.29

Urban

1.25

1.25

0.53

4.08

3.36

1.56

12.05

24.79

2.89

27.96

7.17

0.95

10.92

3.74

Urban

1.25

1.00

0.90

6.44

4.55

2.99

15.00

29.49

4.27

23.28

7.56

0.61

3.95

0.95

Urban

1.25

0.75

1.39

9.19

5.57

5.08

16.58

31.14

5.50

16.83

6.95

0.34

1.23

0.21

Rural

1.00

1.00

0.44

3.96

4.62

1.37

10.14

31.66

2.24

21.98

10.41

0.68

8.50

4.01

Rural

1.00

0.75

0.77

6.40

6.39

2.62

12.64

37.14

3.20

17.24

9.90

0.38

2.48

0.84

Rural

1.25

1.25

0.58

4.56

4.11

1.55

12.91

31.33

1.91

23.77

8.07

0.43

7.89

2.88

Rural

1.25

1.00

1.00

7.26

5.60

2.92

15.78

36.01

2.66

18.21

7.50

0.23

2.24

0.58

Rural

1.25

0.75

1.54

10.46

6.93

4.86

17.13

36.85

3.24

12.13

6.11

0.11

0.55

0.10

Panel

B:stroke

sample

Urban

1.00

1.00

0.98

3.54

4.18

3.03

10.05

21.46

4.85

19.55

9.04

2.77

13.13

7.41

Urban

1.00

0.75

1.50

5.04

5.04

5.29

12.23

24.71

7.18

17.90

10.66

1.91

5.93

2.63

Urban

1.25

1.25

1.31

4.24

4.15

3.53

12.61

21.77

4.40

20.84

6.71

2.20

12.73

5.52

Urban

1.25

1.00

1.98

5.92

4.92

6.01

15.00

24.50

6.35

18.62

7.73

1.47

5.61

1.91

Urban

1.25

0.75

2.70

7.52

5.32

9.19

16.04

24.81

8.15

14.75

7.91

0.87

2.17

0.58

Rural

1.00

1.00

1.14

4.17

5.18

3.19

11.05

26.27

3.68

17.06

9.63

1.70

10.61

6.32

Rural

1.00

0.75

1.75

5.97

6.30

5.50

13.33

29.68

5.25

14.90

10.44

1.01

3.98

1.88

Rural

1.25

1.25

1.55

5.10

5.30

3.70

13.91

27.30

3.11

17.46

7.13

1.21

9.73

4.50

Rural

1.25

1.00

2.32

7.09

6.27

6.16

16.26

29.88

4.30

14.74

7.48

0.70

3.52

1.29

Rural

1.25

0.75

3.15

9.02

6.81

9.25

17.14

29.52

5.28

11.04

6.99

0.35

1.12

0.32

Rural

1.25

0.75

3.15

9.02

6.81

9.25

17.14

29.52

5.28

11.04

6.99

0.35

1.12

0.32

Alldistributio

nsareestim

ated

usingcoefficientestim

ates

inwhich

upcoding

may

occurin

therapyminutes

only,activities

ofdaily

livingindexscores

only,orboth

(i.e.,columns

2and4

ofTable

V).The

predicteddistributio

niscalculated

assumingpaym

entratesandpaym

entdifferentialsim

putin

gallskilled

nursingfacilitiesas

urbanor

ruralandhave

thelistedwage

index.

Allothercharacteristicsof

thepatient

andfacilityareunchanged.

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ultra high category of 11.2 percentage points and very high category of 1.7 percentage points. This generalpattern is found for the hip fracture and stroke sample, for rural-based and urban-based SNF base paymentrates, and regardless if the starting point is 1.00 or 1.25 for the wage index.

A similar type of analysis can be performed to determine if upcoding occurs through ADL index scores.Specifically, we look at the percentage of people in the low, middle, and high ADL index score ranges indicatedby the ‘A’, ‘B’, and ‘C’ in the third letter of each RUG code name. The analysis finds no discernible patternsthat would be consistent with upcoding. For example, in the hip fracture sample with an urban wage index of1.25, the A, B, and C categories are 5.9%, 55.0%, and 39.1%, respectively. However, when the paymentdifferentials are associated with lower wage indices, the proportion of residents in the A and C categories increases(12.3% and 43.9%with 0.75 payment differentials). This result is consistent with the histograms. Further, this gen-eral pattern exists regardless of medical condition and if rural or urban wage differentials are utilized. Therefore,we find no hard evidence to support the hypothesis of upcoding based on ADL index scores.

4.3. Simulated cost implications

In this section, we simulate the cost implications of the financial incentive to upcode. That is, we attempt todetermine Medicare program cost savings in conjunction with the elimination of SNF financial incentives.The challenge with accomplishing this goal is the lack of a control group that did not face the incentive toupcode. This implies the need to simulate savings.

In our simulations, we estimate the expected per diem payment by Medicare under the current financialincentives. This provides us with a baseline payment for comparison. Next, we utilize the fact that we only findevidence of upcoding in terms of therapy minutes to estimate the expected per diem payment by Medicare if thepayment differentials are reduced to reflect the actual cost of providing the additional therapy minutes to movea patient into a higher-reimbursed RUG code. For example, if an urban patient is provided 500 therapy minutesand has an ADL index score of 18, an SNF that provides an additional 220 min of therapy over a 5-day periodcan increase their per diem payment by upcoding. CMS provides an additional $88.91 per day, or 77.9% of theentire payment differential between RUG codes RVC and RUC to cover the cost of the additional therapyminutes provided. However, median wages for therapist staff in the USA indicate that these additional220 min of therapy only cost $21 per day, ignoring payroll taxes and other benefits. We simulate the expectedMedicare per diem payment with reduced payment differentials that reflect the cost of therapy using local areawages obtained from The Bureau of Labor Statistics’ Occupational Employment Statistics for calendar year2005. We simulate savings using median and 90th percentile therapist wage rates.

The savings to Medicare are reported in Table VII. For the hip fracture sample, the baseline expectedpayment for Medicare is $360.14 per day. However, if financial incentives are reduced to reflect the medianand 90th percentile therapist wage rates, the expected per diem payment becomes $329.92 and $331.23, respec-tively. These correspond to savings of 8.4% and 8.0%. Similarly, for the stroke sample, cost savings for themedian and 90th percentile therapy wage assumptions are 12.6% and 12.3%, respectively. If these savings ratesare applied to all SNF admissions, given total payments to SNFs of $18.2bn in 2005, Medicare made additionalpayments to providers in the range of $1.46–2.29bn.

4.4. Sensitivity analysis

Given the complexity of the RUG system in terms of ADL index scores and therapy minutes, it is important tocontrol for both in determining how SNFs respond to financial incentives. In this section, we perform a series ofrobustness checks to make sure that the complexity of the RUG system is not hiding certain details.

In our first sensitivity analysis, we hold constant the ADL index score by restricting the sample to certainADL index score ranges. This allows us to estimate the model by allowing for therapy minute upcoding only,providing a regression that ignores any potential for ADL upcoding. To make sure that SNFs upcode only onthe dimension of therapy minutes, the hip fracture and stroke samples are broken into two separate groups thathave ADL scores in the range of 4–7 and 16–18. Patients in the 4–7 ADL range can be classified into the RUG

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codes of RMA, RHA, RVA, and RUA, whereas patients in the ADL range of 16–18 can be classified into theRUG codes of RMC, RHC, RVC, and RUC (Table I).

Equation (3) is estimated to formally test if SNFs are upcoding on the basis of therapy minutes only in theseselect ADL index score ranges. The lower and upper payment differentials have the same interpretation butonly refer to the payment received by providing fewer or more therapy minutes. The estimates of parametera are reported in Table VIII. Across all four samples, the results have similar signs for the coefficient estimatesfor the payment differential variables, and these signs are consistent with upcoding. The only major differenceacross the regressions are that the effect sizes are larger for the 4–7 ADL samples compared with the 16–18ADL samples. This suggests that the general conclusion that there is upcoding based on therapy minutes isnot affected by restricting the sample to certain ADL groups.

In addition to testing if SNFs upcode solely on the basis of therapy minutes, a similar analysis is performedfor ADL index scores. Specifically, we determine if the payment differentials associated with ADLs affectRUG choice assuming that the patient does not change broad therapy minute categories. These results forthe broad RUG categories of high and ultra high are reported in Table IX. Regressions are also run for the veryhigh category, but the regressions failed to converge because of a lack of concavity of the maximum likelihoodfunction. For patients in the high category of therapy minutes, hip fracture patients seem to be downcoded,whereas stroke patients are upcoded on the basis of ADL payment differentials. However, neither of these re-sults is statistically significant. In contrast, the payment differentials are statistically significant and consistent

Table VIII. Supplemental regression results for therapy minute code selection decisions

Hip fracture sample Stroke sample

4–7 ADLs 16–18 ADLs 4–7 ADLs 16–18 ADLs

Therapy minute lower payment differential 0.312*** (0.075) 0.086*** (0.023) 0.172*** (0.044) 0.072*** (0.013)Therapy minute upper payment differential �0.044*** (0.013) 0.000 (0.003) �0.029*** (0.008) �0.007* (0.004)Number of observation 14,920 105,736 50,260 170,088Number of patients 3,730 26,434 12,565 42,522

Standard errors adjusted for clustering at the state level are reported in parentheses. All regressions are estimated using a mixed logit modeland include patient and skilled nursing facility (SNF) characteristics. Patient characteristics include gender, age, race, education, short-termand long-term memory loss, cognitive impairment, and indicators for the following medical conditions: diabetes, heart disease, cardiac dys-rhythmia, heart failure, chronic obstructive pulmonary disease, dementia, anxiety, and depression. SNF characteristic variables includeownership, number of beds, part of a multifacility chain, payer mix, occupancy rate, acuity level, percent dementia, and nurse staffinglevels. In addition, regional indicator variables of New England, Mid-Atlantic, Midwest, South, Mountain, and Western states are included.ADL, activities of daily living.***p< 0.01, **p< 0.05, *p< 0.1.

Table VII. Simulated savings per day: therapist cost approach

Expected payment ($)

Savings

Dollars Percent

Hip fracture sampleCurrent system 360.14 — —Cost-based payment differentials (using median wages) 329.92 30.22 8.39Cost-based payment differentials (using 90th percentile wages) 331.23 29.69 8.03

Stroke sampleCurrent system 376.04 — —Cost-based payment differentials (using median wages) 328.52 47.52 12.64Cost-based payment differentials (using 90th percentile wages) 329.73 46.31 12.32

The estimated payment for the current system is the expected per diem payment by Medicare for the entire sample based on the actualpayment differentials for each skilled nursing facility in 2005. The estimated per diem payment for the cost-based payment differentialsassumes that the payment differential for each SNF is based on the median (or 90th percentile) therapist wage and number of minutesrequired to classify a patient into an adjacent resource utilization group.

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with upcoding for both samples when patients are in the ultra high category. This would suggest that SNFs mayupcode patients using ADL index scores, but only within certain therapy minute categories. However, given thelack of evidence of upcoding based on ADLs in our main regressions, there is little evidence to suggest thatupcoding occurs through manipulation of ADL index scores.

Finally, in our empirical model, the payment differentials reflect changing the RUG by only one code level(e.g., RVC to RUC or RUB to RUC). In theory, SNFs could upcode by more than one coding level. This isunlikely for a few reasons. In the dimension of ADL index scores, the threat of readmission reduces theincentive to upcode someone from a very low level of severity to a very high one. Because ADL index scoresimprove slowly over time, if at all, a readmission of the patient with a low ADL index code is a clear signal ofupcoding. In terms of therapy minutes, an SNF that reports a significant proportion of patients receiving the highestnumber of therapy minutes, relative to peers, could draw attention from auditors. As a final robustness check, weallowed for upcoding by more than one RUG level. The coefficient estimates for these models are consistent withupcoding; however, we find no statistically significant effects for upcoding by more than one level.

5. CONCLUSIONS

This paper is the first to directly test the hypothesis that there is upcoding in the skilled nursing industry bylooking at how SNFs respond to regional variation in reimbursement rates. We provide empirical evidence thatgeographic adjustment factors used by the CMS to modify payments for the cost of care are imperfect andexploit this fact to determine how SNF code selection responds to financial incentives. This upcoding can occurwhen SNFs vary thresholds used to increase a patient’s ADL index score or by providing more therapy minutesto increase payments from Medicare.

We do not find sufficient evidence to conclude that SNFs upcode through ADL index scores, despite the factthat ADL index scores are costless to increase. A likely reason for this result is the verifiability of ADLs, asthere are reporting requirement to the CMS on three different forms in addition to the fact that many patientsare readmitted to SNFs in a short time. Because ADL index scores improve slowly over time, the threat ofauditing may be credible enough for SNFs to not upcode on the basis of ADL index scores. In contrast,histograms of the number of therapy minutes patients receive and our empirical analysis suggest that SNFsprovide greater numbers of therapy minutes to increase reimbursement. Our simulation suggests that thisupcoding equates to over $1.46–2.29bn in additional payments by Medicare in 2005.

The key difference between ADL index scores and therapy minutes is verifiability. Whereas there aremultiple mechanisms that provide credible threat of audits for ADL index scores, there is significant ambiguityin the number of therapy minutes a patient should receive. As pointed out by Newhouse (2003/2) and the OIG

Table IX. Supplemental regression results for ADL index score selection decisions

Hip fracture sample Stroke sample

High category Ultra high category High category Ultra high category

ADL lower payment differential �10.969 (9.583) 32.532*** (12.172) 2.713 (7.088) 27.282*** (7.822)ADL upper payment differential 9.964 (8.670) �7.638*** (2.882) �2.419 (6.418) �6.414*** (1.848)Number of observations 52,206 45,651 72,231 94,758Number of patients 17,402 15,217 24,077 31,586

Standard errors adjusted for clustering at the state level are reported in parentheses. All regressions are estimated using a mixed logit modeland include patient and skilled nursing facility (SNF) characteristics. Patient characteristics include gender, age, race, education, short-termand long-term memory loss, cognitive impairment, and indicators for the following medical conditions: diabetes, heart disease, cardiac dys-rhythmia, heart failure, chronic obstructive pulmonary disease, dementia, anxiety, and depression. SNF characteristic variables includeownership, number of beds, part of a multifacility chain, payer mix, occupancy rate, acuity level, percent dementia, and nurse staffinglevels. In addition, regional indicator variables of New England, Mid-Atlantic, Midwest, South, Mountain, and Western states are included.ADL, activities of daily living.***p< 0.01, **p< 0.05, *p< 0.1.

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(2010), post-acute care does not have standardized treatment guidelines in terms of therapy minutes. Thisallows for flexibility in how providers determine reimbursement codes and can directly result in upcoding.Other than direct auditing of medical records, there is currently little ability to use administrative data to detecttherapy minute upcoding other than comparing RUG code selection distributions across peer SNFs.

By establishing standardized treatment guidelines, the CMS can use administrative data to create a crediblethreat of audit, reducing the incentive to upcode. The OIG suggested development of such guidelines in 2010by using case-mix adjusters based on hospital diagnosis data (OIG, 2010), but the CMS responded bydismissing this suggestion. CMS argued that SNFs do not receive this information in a timely manner. Analternative is to create measureable case-mix adjusters that can be recorded in MDS and reported on case-mix documentation that is tied to a range of recommended number of therapy minutes. By creating guidelines,the CMS can then search over MDS records to identify significant outliers from treatment guidelines. If anygiven facility is found to have a significant number of outliers in a predetermined period, then the facilitycan be flagged for a closer inspection and potentially an audit. This type of system would provide a crediblethreat of an audit, reducing the incentive to upcode.

Although this study finds that SNFs respond to the financial incentive to upcode, we do not attempt to identifythe underlying motivation to upcode. For example, SNFs may upcode to increase profits, to pay for the ‘true’ costof therapy due to imperfections in how reimbursement rates reflect the cost of care, or for other reasons. Even withthe increased credibility associated with audits, Medicare will mostly likely continue to face financial burdensassociated with upcoding, given that other factors, such as ownership and peer effects, may play a role in codeselection. Furthermore, because upcoding occurs with the provision of additional therapy minutes, upcoding couldtranslate into better clinical outcomes for patients, resulting inMedicare cost savings after discharge. However, thistype of calculation is beyond the scope of this paper. Even so, not all therapy minutes provided will be at theclinically efficient level, resulting in significant and unnecessary Medicare costs.

AKNOWLEDGEMENTS

We acknowledge helpful comments fromMichael Darden, David Grabowski, Rachel Werner, Myeong-Su Yun,seminar participants at Ball State University, Miami University, Tulane University, University of South Florida,the 4th Biennial Conference of the American Society of Health Economists, and two anonymous referees. Wealso thank Edward Norton for helpful advice and acknowledge the Farmer School of Business for providingfunding for the data.

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