The Value-Based Payment Modifier: Program Outcomes and Implications for Disparities

In January 2017, the Centers for Medicare & Medicaid Services (CMS) implemented the Merit-based Incentive Payment System (MIPS), establishing a new payment system for clinicians in the fee-for-service Medicare program (1). As part of a broader push to link provider payments to value (25), the MIPS is a pay-for-performance program that intends to reward clinicians for improving quality of care and reducing spending. Although the effects of this program will not be known for several years, its basic design is similar to that of its predecessor, the Value-Based Payment Modifier (Value Modifier [VM]) (6). Each year from 2013 through 2016, the VM assessed the performance of physician practices on a set of quality and spending measures and adjusted Part B payment rates in the Medicare Physician Fee Schedule 2 years later on the basis of these performance scores (7). In 2013, practices with 100 or more clinicians were required to meet reporting requirements or incur a small reduction in 2015 payment rates, but exposure to the VM (that is, performance-based payment adjustments) was optional (8). In 2014, the VM became mandatory for all practices with 10 or more clinicians, except those participating in alternative payment models, such as Medicare's Accountable Care Organization (ACO) programs (9). Practices with 100 or more clinicians were subject to upward, downward, or neutral performance-based payment adjustments, those with 10 to 99 were subject to upward or neutralbut not downwardadjustments, and those with fewer than 10 were unaffected (3, 10). In 2015, all practices with 10 or more clinicians were exposed to full VM incentives (both penalties and bonuses) (11). Base payment adjustments ranged from 2% to 2% on the basis of 2014 performance and from 4% to 4% on the basis of 2015 performance, but high-performing practices have received much higher bonuses (for example, rate increases of 16% to 32% in 2016), because the VM's budget neutrality provision stipulated that penalties for failing to meet reporting requirements be redistributed as bonuses (12, 13). To date, many performance measures used in the VM and MIPS have been adjusted for only a limited set of patient characteristics (1416), raising concerns that practices' performance scores may partly reflect differences in their patients' clinical or social characteristics, rather than only differences in quality of care (1722). Because budget neutrality provisions in these programs require penalties and bonuses to offset, inadequate risk adjustment might result in sustained and unwarranted transfers of resources from practices serving sicker or more socially disadvantaged patients to those serving healthier or more affluent patients (2327). In evaluating the merits of pay-for-performance programs, it therefore is important to consider both the behavioral response elicited by program incentives and the implications of inadequate risk adjustment for health care disparities. In this study, we assessed differences in performance on quality and spending measures associated with the exposure of practices with 10 or more and those with 100 or more clinicians to partial or full VM incentives in 2014, respectively, as well as performance differences associated with the exposure of practices with 100 or more clinicians to a second year of incentives in 2015. In a second set of analyses, we examined the effect of adjusting for additional patient characteristics on practice rankings and on performance differences between practices with larger proportions of low-income and medically complex patients and practices with smaller proportions of such patients. Methods Study Design For our first set of analyses, we used a cross-sectional regression discontinuity design to assess differences in spending and quality between practices above and below the size thresholds determining exposure to the VM. This design exploits the fact that exposure to performance incentives in the VM differed between practices above and below specific thresholds but other determinants of spending and quality likely did not, enabling an inference similar to that from a randomized study (28). Because too few observations may exist within a narrow range of a threshold to support comparisons, regression discontinuity studies typically use broader ranges of data and regression analysis to estimate discontinuities (that is, level shifts) in outcomes above versus below a threshold. Thus, we analyzed data from practices with 50 to 150 clinicians (for the threshold of 10 or more clinicians) and 2 to 30 clinicians (for thresholds of 10 or more clinicians) to estimate discontinuities in spending and quality. We assumed that the relationship between practice size and performance would have an uninterrupted approximately linear trend across these thresholds in the absence of the VM. In our second set of analyses, we assessed practice performance before versus after adjusting for additional patient characteristics not included in the risk-adjustment methods used by CMS in the VM or MIPS (14, 15). We assessed performance differences between practices serving larger proportions of low-income and medically complex patients and those serving smaller proportions of such patients and compared these differences before and after the additional adjustments. We also assessed changes in the relative performance ranking of practices after the additional adjustments. This second set of analyses illustrates the implications of limited risk adjustment for health care disparities, not only in the VM but also in the MIPS. Data Sources and Study Population We analyzed claims and enrollment data in 2014 and 2015 for a random 20% sample of beneficiaries who were continuously enrolled in Parts A and B of fee-for-service Medicare in the year of interest (while alive in the case of decedents) and the preceding year (to assess established diagnoses). Following methods used by CMS for the VM, we attributed each beneficiary to the practice (defined by CMS as a taxpayer identification number [TIN]) that accounted for the largest share of allowed charges for that beneficiary's office visits during the study year (Supplement) (29). Beneficiaries without an office visit during the year (13%) were excluded. To exclude practices unaffected by the VM, we used CMS data on ACO participants to remove practices participating in the Pioneer model or Medicare Shared Savings Program (Supplement) (30). Supplement. Supplementary Online Content Practice Exposure to the VM To determine practice size and thus exposure to VM incentives, we used the 2014 Medicare Provider Practice and Specialty file to attribute each clinician to the TINs under which they billed for Part B services (Supplement) (31). We calculated the total number of clinicians billing under each TIN and created indicators for 3 size categories: fewer than 10 clinicians (no exposure), 10 to 99 clinicians (exposed to potential bonuses only), and 100 or more clinicians (exposed to potential bonuses and penalties). Each category had a different exposure to VM incentives in 2014. Our method for determining practice size closely followed the approach used by CMS to determine practice size for the VM and yielded a total number of practices with 100 or more clinicians that was very similar to that reported by CMS (Supplement Table 1) (6, 9, 32). Outcome Variables We examined 3 annual measures of quality and spending that CMS assessed as core performance measures for all practices subject to the VM: admissions for ambulatory caresensitive conditions (ACSCs) (14), total Medicare Part A and Part B spending per beneficiary (15), and all-cause readmissions within 30 days of hospital discharge (Supplement) (16). Although annual mortality was not included as a performance measure in the VM, we assessed it as an additional measure that may be particularly sensitive to risk adjustment and can be interpreted as a health outcome more reliably than utilization-based quality measures (for example, admissions and readmissions often may be appropriate and improve health). Patient Characteristics We used Medicare enrollment data to determine age, sex, and race/ethnicity of beneficiaries; whether end-stage renal disease was present; whether beneficiaries were enrolled in Medicaid (dual eligibility); and whether disability was the original reason for Medicare entitlement. We used the Chronic Conditions Data Warehouse to determine the presence of 27 chronic conditions before each study year. Finally, for each beneficiary, we calculated a Hierarchical Condition Category (HCC) risk score based on enrollment information (including Medicaid coverage) and clinical diagnoses from claims in the preceding year (33). Statistical Analysis For each outcome, we conducted a regression discontinuity analysis to isolate differences associated with exposure of practices in 2014 to bonuses and penalties above the threshold of 100 or more clinicians (vs. only bonuses below the threshold) and to bonuses above the threshold of 10 or more clinicians (vs. neither bonuses nor penalties below the threshold). Specifically, we fitted a patient-level linear regression model to estimate the difference in performance between practices above each threshold and those below it, adjusting for the linear relationship between the outcome and practice size (number of clinicians) and for patients' clinical and sociodemographic characteristics (Supplement). This adjusted difference (or adjusted discontinuity) may be interpreted as the difference in performance attributable to VM incentives. We repeated our analysis of the threshold of 100 or more clinicians with data from 2015, when practices with 10 to 99 clinicians were also exposed to penalties, to isolate performance differences associated with 2 years of exposure to full VM incentives (above the threshold) versus 1 year of exposure to full incentives (below the threshold). We conducted several analyses t