Mark Aaron Unruh, PhD

The Patient-Driven Payment Model (PDPM) addresses perverse incentives in Medicare’s prior payment system for skilled nursing facilities by prioritizing patient needs over service volume.

The PDPM has introduced new incentives that may be problematic, potentially leading to unintended consequences.

Regulators should maintain close oversight of billing by skilled nursing facilities and patient outcomes as the PDPM takes effect to mitigate potential unintended consequences.

In October 2019, Medicare, which spends $30 billion on 2.4 million skilled nursing facility (SNF) stays annually, began reimbursing facilities using the Patient-Driven Payment Model (PDPM).

 The PDPM replaces the Resource Utilization Groups (RUG) system, which had been used since 1998 and which many believe created perverse incentives that contributed to rapid growth and unwarranted variation in Medicare spending on postacute care.

 The PDPM was implemented to address these problems, and although it is a step forward, it also creates new risks for unintended consequences.

 This commentary explores these risks and what can be done to mitigate them.

Perverse Incentives in the Previous Payment System for SNFs

Medicare has historically reimbursed SNFs using the RUG system, in which the level of payments varied across 5 categories based on the number of therapy minutes provided weekly (

This system had sharp cutoffs and large payment differentials for therapy-minute thresholds separating each RUG category. Some SNFs are thought to have taken advantage of these features to increase revenue by moving patients into higher RUG payment categories, contributing to rapid growth in Medicare expenditures.

 Under the RUG system, an SNF received $438 per day, on average, for a typical patient needing assistance with 6 to 10 activities of daily living in the “very high” RUG category (Table 1). If this same patient were placed in the “ultra-high” category, the per diem payments increased to $601, on average. Between 2002 and 2016, the use of the ultra-high category increased from 7% to 63% of all SNF days reimbursed by Medicare

—a 9-fold increase. There was no corresponding change in case mix during this period.

Large payment differentials among RUG categories also led to “high billing,” in which some SNFs systematically submitted claims just surpassing the therapy-minute threshold required for a payment category. In the earlier example, an SNF providing a patient with 719 minutes of therapy in a week would receive the very high category payment rate ($438), but providing 720 minutes would raise payment to the ultra-high category rate ($601). The $163, or 37%, differential in the per diem payment between the very high and ultra-high categories created a strong incentive for SNFs to provide just enough therapy to move a patient into the higher-paying category, regardless of whether they required such intensive therapy. In a 2016 analysis, we found that nearly one-fifth of all SNFs billed 90% or more of their ultra-high category claims within 10 minutes of the payment threshold (ie, 720-730 therapy minutes).

Addressing Perverse Incentives in the RUG System Through the PDPM

As its name suggests, the PDPM is based on patient characteristics. Instead of paying SNFs based on the volume of therapy provided, the PDPM uses diagnoses and other clinical factors to set payment rates for 5 components that are summed to determine the per diem reimbursement: physical therapy, occupational therapy, speech language pathology services, nursing services, and nontherapy ancillary conditions and services. A sixth component, based on SNF resources used during the stay, is also added to this per diem reimbursement but does not vary by beneficiary risk.

Whereas the RUG system incentivized longer SNF stays and more therapy, the PDPM incentivizes shorter stays with less therapy through an adjustment factor for physical therapy, occupational therapy, and nontherapy ancillary conditions and services. After the first 20 days of the SNF stay, the adjustment factor is reduced by 2% every 6 days.

Potential Unintended Consequences of the PDPM

Although the PDPM addresses a key feature of the RUG system by replacing payment based on the volume of services delivered with payment based on patient characteristics, it has also introduced other incentives that may be problematic (

). For example, the PDPM’s component for nontherapy ancillary conditions and services, based on a list of 50 comorbidities and services, is weighted upward by 300% during the first 3 days of the stay. Ostensibly, this makes sense because the cost of providing SNF care is substantially higher at the beginning of a patient’s stay. But certain comorbidities and services in this payment component can greatly increase reimbursements, and they thus carry the risk of being overreported and overutilized. An SNF located in an urban area, for instance, receives per diem payments that are $175 higher during the first 3 days for a patient with diabetes and chronic obstructive pulmonary disease compared with payments for a patient without these 2 conditions.

 The provision of intravenous medication alone can increase the per diem rate by the same amount. SNFs are likely to invest in developing expertise in 

International Classification of Diseases, Tenth Revision

 coding, which was not required under the RUG system; therefore, as with other value-based payment models, regulators should monitor for gaming related to upcoding motivated by the PDPM.

A related concern is that the prospect of higher payments for greater patient complexity could lead some SNFs to selectively admit sicker patients, who may require levels of care that are beyond a given SNF’s abilities. If SNFs are willing to accept higher-acuity patients, hospitals—incentivized to reduce lengths of stay&mdash;may be willing to discharge patients “sicker and quicker.” Previous research has raised concerns about such practices.

 Substituting SNF care for hospital care has led to higher readmission rates for patients with certain conditions. Although financial penalties for hospitals with excessive readmissions may deter unsafe discharges to SNFs, these policies are increasingly under scrutiny themselves for potentially contributing to adverse consequences, including higher mortality rates for patients with certain conditions.

Finally, compared with the RUG system, the PDPM incentivizes shorter SNF stays and less therapy, which may have negative consequences for some patients. Although shorter SNF length of stay has not been associated with worse outcomes,

 findings of prior research suggest that less therapy can lead to decreased functional improvement, higher readmission rates, and a lower likelihood of discharge to the community.

The PDPM brings welcome changes to SNF reimbursement by prioritizing patient needs over service volume, but it also carries the risk of new unintended consequences. These potential problems, which are features of other value-based payment systems, should be closely monitored and rigorously studied. Accountable care organizations and episodic payments that include postacute care offer some hope, as they incentivize healthcare organizations to discharge patients to SNFs only when appropriate and to refer patients to high-quality facilities. Findings of recent studies suggest that these payment programs have reduced postacute care spending, primarily through lower SNF utilization, without adversely affecting outcomes.

 The recently implemented SNF Value-Based Purchasing Program, which imposes financial penalties on SNFs with high hospital readmission rates, may also discourage facilities from accepting patients requiring care beyond their capabilities. It is not yet clear whether this negative incentive (maximum 2%) is large enough to prevent SNFs from accepting patients they are not equipped to care for. Regardless, policy makers should maintain close oversight of SNF billing practices and patient outcomes as the PDPM takes effect to mitigate potential unintended consequences.

 Division of Health Policy and Economics, Department of Healthcare Policy and Research (MAU, DK, HYJ), and Department of Medicine (DK), Weill Cornell Medical College, New York, NY.

 The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (MAU, DK, HYJ); acquisition of data (MAU, HYJ); analysis and interpretation of data (MAU, DK, HYJ); drafting of the manuscript (MAU, DK, HYJ); critical revision of the manuscript for important intellectual content (MAU, DK, HYJ); statistical analysis (MAU, HYJ); and supervision (HYJ).

 Hye-Young Jung, PhD, Weill Cornell Medical College, 402 E 67th St, New York, NY 10065. Email: arj2005@med.cornell.edu.

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Articles

The Patient-Driven Payment Model addresses perverse incentives in Medicare’s previous payment system for skilled nursing facilities, but it includes new incentives that may be problematic.

The Patient-Driven Payment Model: Addressing Perverse Incentives, Creating New Ones

 To measure the impact of hospital participation in Meaningful Use (MU) on disparities in 30-day readmissions associated with race.

 A retrospective cohort study that compared the likelihood of 30-day readmission for Medicare beneficiaries discharged from hospitals participating in Stage 1 of MU with the likelihood of readmission for beneficiaries concurrently discharged from hospitals that were not participating in the initiative.

Inpatient claims for 2,414,205 Medicare beneficiaries from Florida, New York, and Washington State were used as the primary data source. The study period (2009-2013) included at least 2 years of baseline data prior to each hospital initiating participation in MU. Estimates were derived with linear regression models that included hospital and time fixed effects. By including both hospital and time fixed effects, estimates were based on discharges from the same hospital in the same time period.

 MU participation among hospitals was not associated with a statistically significant change in readmissions for the broader Medicare population (percentage points [PP], 0.6; 95% CI, —0.2 to 1.4), but hospitals’ participation in the initiative was associated with a lower likelihood of readmission for African American beneficiaries (PP, –0.9; 95% CI, –1.5 to –0.4).

 Hospital participation in MU reduced disparities in 30-day readmissions for African American Medicare beneficiaries.

Hospital participation in Meaningful Use (MU) was not associated with declines in 30-day readmissions for the broader Medicare population.

Hospital participation in MU was associated with a decline in readmissions of 0.9 percentage points for African American Medicare beneficiaries.

Readmissions among Medicare beneficiaries are common and often reflect care of poor quality.

 Racial minorities are more likely to be readmitted than other Medicare beneficiaries.

 Policy makers have been optimistic that hospitals’ adoption of health information technology (IT) will lead to lower readmission rates,

 and reducing health disparities is a priority of the federal Meaningful Use (MU) initiative.

 A recent study evaluated the impact of hospitals’ participation in MU on readmission rates of Medicare fee-for-service (FFS) beneficiaries,

 but the relationship between hospitals’ participation in the initiative and racial disparities in readmissions has not been examined.

The MU initiative was created through the Health Information Technology for Economic and Clinical Health (HITECH) Act. Approximately $30 billion was made available through HITECH to promote the adoption and use of electronic health records (EHRs) certified by the Office of the National Coordinator for Health Information Technology.

 Hospitals have received MU incentive payments through the Medicare and Medicaid EHR Incentive Programs. For Stage 1 of the incentive programs, attestation requirements focused on capturing patients’ clinical data electronically and making health information available to patients electronically

 by requiring hospitals to meet 11 core objectives and 5 additional objectives selected from a menu of 10.

 Hospitals were required to meet performance thresholds for the objectives and report their performance on them for 90 continuous days in order to receive incentive payments. Ongoing financial penalties imposed on hospitals that do not attest to MU requirements have further incentivized participation. 

Hospitals have been responsive to the MU incentive payments and financial penalties, with 98% of eligible hospitals participating in the initiative as of 2016.

 MU participation among hospitals has not been associated with reductions in readmissions occurring within 30 days of discharge.

 However, given that racial minorities have higher readmission rates

 and that reducing disparities is a priority of the MU initiative,

 it is possible that hospitals’ participation in the initiative has had a greater impact on readmissions of racial minorities. The objective of this study was to measure the impact of hospital participation in MU on disparities in 30-day readmissions associated with race.

The likelihood of 30-day readmission for Medicare beneficiaries discharged from hospitals participating in MU was compared with the likelihood of readmission for concurrent beneficiaries discharged from hospitals that were not participating in the initiative. The study period covered 2009 through 2013. Hospitals in our sample initiated MU participation at different times following the implementation of the initiative in 2011. The differential timing of hospitals’ participation in MU was used to estimate its impact on readmissions.

It is possible that hospitals that were early participants in MU differed from later participants and nonparticipants in ways that affected the likelihood of readmission. Two steps were taken to address this issue. First, regression models included hospital fixed effects to control for permanent differences among hospitals. Second, only hospitals that began MU participation by the end of the study period were included in the sample.

The primary data sources for the study were State Inpatient Databases for Florida, New York, and Washington State from the Healthcare Cost and Utilization Program (HCUP) within the Agency for Healthcare Research and Quality. HCUP State Inpatient Databases include all discharges, regardless of payer, from community hospitals in a given state. The American Hospital Association Annual Survey provided information on hospitals’ use of EHRs, and Stage 1 MU payment records from the Medicare and Medicaid EHR Incentive Programs were used to identify participants. The unit of analysis was the hospitalization. Therefore, some patients were observed multiple times over the study period.

The study sample included 2,414,205 unique Medicare beneficiaries 65 years and older, in both FFS and Medicare Advantage, who were discharged to home or postacute care from 291 nonfederal hospitals. The MU incentive programs were implemented in 2011. Therefore, the study period (2009-2013) included at least 2 years of baseline data before each hospital began MU participation.

The outcome was an indicator of all-cause 30-day readmission derived from the HCUP algorithm for hospital revisits.

 Patient identifiers in the State Inpatient Databases only allowed a readmission to be identified if it took place in the same state as the index admission. For Washington, patient identifiers were not consistent from year to year and, therefore, index admissions occurring in December were excluded for this state. Control variables included indicators for age (65-69, 70-74, 75-79, 80-84, 85 years and older), gender, race, quartiles of median household income for the patient’s zip code, Deyo-Charlson Comorbidity Index scores (1, 2, 3, 4, 5 or higher),

 weekend admission, and a continuous measure of length of stay.

 Race categories for white and African American beneficiaries have shown reasonable reliability in administrative claims, but other race and ethnicity categories are less reliable.

 Therefore, race was categorized as white, African American, and other nonwhite race. Additionally, a binary indicator for hospital participation in MU was included in the model. Once a hospital received a Stage 1 payment from either the Medicare or Medicaid EHR incentive program, the measure identified the hospital as a MU participant from that year forward through the rest of the study period. The initial Stage 1 incentive payment was made to a hospital up to 8 weeks after it completed the 90-day attestation period.

 Therefore, each hospital had been meeting MU objectives for up to 5 months when the initial Stage 1 payment was received. The determinants of interest were interaction terms for a hospital’s participation in MU and patient race categories. Estimates associated with the interaction terms indicated whether hospital MU participation differentially impacted the likelihood of readmission for racial minorities.

Linear probability models with hospital and year fixed effects were used for estimation. This approach controls for permanent differences among hospitals by including a dummy variable for each. By including both hospital and time fixed effects, estimates were based on discharges from the same hospital in the same time period. However, the inclusion of fixed effects in the regression model results in wider confidence intervals for the estimated coefficients. 

In a secondary analysis, an indicator for EHR use in the year prior to MU participation was added to the specification. This was done to examine whether any changes in the likelihood of readmission were due to hospitals’ MU participation or simply use of an EHR. The standard errors were adjusted for clustering at the level of the hospital in both models.

 SAS version 9.2 (SAS Institute; Cary, North Carolina) and Stata MP version 14 (StataCorp; College Station, Texas) were used for analyses.

The study was approved by the Human Protections Office of Weill Cornell Medical College.

The mean age of Medicare beneficiaries in our sample was 78.2 years, and 56.6% were female (

). The racial composition of the sample was 77.2% white, 8.4% African American, and 12.9% other nonwhite race; 1.4% of the sample was of unknown race. Slightly more beneficiaries resided in middle-income zip codes than low- or high-income zip codes, with 22% in the lowest quartile, 26% each in the second and third quartiles, and 25% in the highest quartile. For hospital discharges, the mean score on the Deyo-Charlson index was 2.3, mean length of stay was 5.4 days, and 19.9% were weekend admissions. Among the hospitals in our sample, the mean number of licensed beds was 341.2, the majority (71.9%) were not-for-profit, 23.8% were government hospitals, and 39.5% were affiliated with a medical school. In 2011, the first year of the Medicare and Medicaid EHR Incentive Programs, 39.6% of the hospitals in our sample had an EHR but did not participate in MU and 33.5% initiated participation in the initiative. In 2012, the proportion of hospitals with EHRs and no MU participation fell to 7.1% and the proportion of hospitals participating in the initiative grew to 92.4%.

Unadjusted 30-day readmission rates for all Medicare beneficiaries rose in the first 2 years of the study period (

), climbing from 20.6% in 2009 to 21.1% in 2011, when the MU initiative was first implemented. Readmission rates then declined to 19.6% in 2013, the last year of the study period. A similar trend in unadjusted 30-day readmissions was observed for white beneficiaries, although the annual rates were lower in magnitude than rates for all Medicare beneficiaries in each year of the study period. For African American beneficiaries, readmission rates were higher throughout the study period compared with the rates for all beneficiaries. However, after remaining at 24.3% in 2009 and 2010, the readmission rate for African American beneficiaries fell to 23.8% in 2011 and 22.1% in 2013. Unadjusted readmission rates for beneficiaries who were not white or African American followed the same trend as those for all Medicare beneficiaries, but were higher in each year.

) did not indicate reductions in readmissions associated with hospital participation in MU for the overall Medicare population in our sample (percentage points [PP], 0.6; 95% CI, —0.2 to 1.4). The estimate for the interaction between MU participation and African American race (PP, –0.9; 95% CI, –1.5 to –0.4) indicated statistically significant declines in readmissions compared with white beneficiaries, but the interaction of MU participation and other nonwhite race did not indicate a reduction in these events compared with white beneficiaries (PP, –0.4; 95% CI, –0.8 to 0.0). The decline of 0.9 PP represents a 3.7% relative decrease in readmissions for African American beneficiaries using the unadjusted rate prior to MU (24.3%) as the baseline. Similar to our primary analysis, results from our secondary analysis that controlled for prior EHR use did not suggest a relationship between hospitals’ participation in the initiative and readmissions among the broader Medicare population (PP, 1.5; 95% CI, –0.1 to 3.1), but indicated a decline in the likelihood of readmission among African American beneficiaries (PP, –0.9; 95% CI, –1.5 to –0.4). Additionally, a statistically significant decline in readmissions for beneficiaries of other nonwhite race was associated with hospitals’ participation in MU in the secondary analysis (PP, –0.7; 95% CI, –1.1 to –0.3).

Hospital participation in MU was not associated with a reduced likelihood of readmission for the overall Medicare population in our sample, but it was associated with reductions in these events for African American beneficiaries. The results of a secondary analysis that controlled for use of an EHR prior to a hospital’s MU participation were consistent with these findings.

Our study represents an important addition to the existing literature because, despite the nation’s $30-billion investment in health IT through the MU initiative,

 evaluations of provider participation have shown little evidence of improvements in the quality of care.

 It is important to note that previous studies of provider participation in MU, including this one, have only examined Stage 1 of the initiative. Use of more advanced functions, such as interoperability of EHRs, in addition to improving patient outcomes were not emphasized until later stages of the initiative. However, better documentation of patients’ clinical information and improved access to that information through Stage 1 attestation requirements may have affected patients at higher risk of readmission,

 including racial minorities, more than other patients. Additionally, MU participation may have led to reductions in readmissions among broader groups of patients after the study period as hospitals gained more experience with EHRs, including increased use of health information exchange, and learned to use these systems to improve patient outcomes.

Our study has limitations to consider. Due to our reliance on administrative claims, it is possible that unobserved differences among hospitals influenced our results. Our inclusion of hospital fixed effects and limiting the sample to hospitals that initiated participation in MU prior to the end of the study period mitigated the potential for bias, but we cannot rule out this possibility. For example, these approaches would not have controlled for unobserved differences among hospitals or patients that changed over time. Additionally, records for incentive payments were used to identify the timing of MU participation, which likely introduced some measurement error. This would introduce bias toward the null leading to estimates that understated the impact of hospitals’ MU participation on readmissions. Differential 30-day mortality rates may also have influenced estimates of readmission. With the HCUP State Inpatient Databases, it was not possible to identify beneficiaries who died after hospital discharge. Although 30-day mortality is a competing risk, previous studies of Medicare readmissions have not shown mortality rates to be a considerable influence on estimates of hospitalization rates, including readmissions.

 We also included only 3 race categories: white, African American, and other nonwhite race. Combining multiple groups into “other nonwhite race” could have masked differences among these groups and introduced measurement error, biasing the estimate toward no effect. It was also not possible to determine whether a patient’s primary care physician was notified of a hospitalization or if the physician had an interoperable EHR that was capable of receiving the patient’s information electronically from the hospital. Timely follow-up care after hospital discharge and better coordinated care enabled through the electronic exchange of a patient’s health information could reduce the likelihood of readmission. However, findings of a previous study indicated that physician participation in MU did not lower the likelihood of 30-day readmissions for Medicare beneficiaries.

 Lastly, our sample only included hospitalized Medicare beneficiaries from Florida, New York, and Washington. However, these states are located in different regions of the country and have diverse populations.

In addition to the limitations listed above, other policy initiatives that may have been implemented during the study period with the goal of reducing readmissions may have affected our estimates. These include Medicare’s Hospital Readmissions Reduction Program (HRRP) and the Medicare Shared Savings Program (MSSP) for accountable care organizations (ACOs). Hospitals could also potentially have used EHRs adopted through MU participation to improve their performance in these initiatives. The HRRP was implemented in October 2012 and the first MSSP ACOs began in April 2012. If early participants in MU were more likely to participate in these programs, which led to more resources being focused on patients with a higher likelihood of readmission, such as racial minorities, our estimates may overstate the impact of hospitals’ participation in MU.

To our knowledge, this is the first study of the relationship between hospital participation in MU and racial disparities in readmissions. We found a statistically significant relationship between hospitals’ participation in the initiative and reductions in racial disparities associated with 30-day readmissions among Medicare beneficiaries. Additional study is needed to determine if disparities were further reduced as hospitals gained more experience with EHRs and outcomes were emphasized in later stages of MU.&ensp;

The authors are grateful for programming and data management support from Phyllis Johnson, MBA.

 Department of Healthcare Policy and Research, Weill Cornell Medical College (MAU, H-YJ, RK), New York, NY; Department of Health Policy and Management, Richard M. Fairbanks School of Public Health, Indiana University (JRV), Indianapolis, IN; Regenstrief Institute, Inc (JRV), Indianapolis, IN.

 National Institute on Aging (R03AG046671, PI: Mark A. Unruh).

Concept and design (MAU, H-YJ, RK, JRV); acquisition of data (MAU); analysis and interpretation of data (MAU, H-YJ, RK, JRV); drafting of the manuscript (MAU, H-YJ, RK, JRV); critical revision of the manuscript for important intellectual content (MAU, H-YJ, RK, JRV); statistical analysis (MAU, H-YJ, JRV); obtaining funding (MAU); and supervision (MAU).

 Mark Aaron Unruh, PhD, Weill Cornell Medical College, 402 E 67th St, New York, NY 10065. Email: mau2006@med.cornell.edu.

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Hospital participation in Meaningful Use was associated with reduced disparities in 30-day readmissions for African American Medicare beneficiaries.

Mark Aaron Unruh, PhD

Articles

The Patient-Driven Payment Model: Addressing Perverse Incentives, Creating New Ones

Hospital Participation in Meaningful Use and Racial Disparities in Readmissions