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

Abstract

Background: When risk adjustment is inadequate and incentives are weak, pay-for-performance programs, such as the Value-Based Payment Modifier (Value Modifier [VM]) implemented by the Centers for Medicare & Medicaid Services, may contribute to health care disparities without improving performance on average.

Objective: To estimate the association between VM exposure and performance on quality and spending measures and to assess the effects of adjusting for additional patient characteristics on performance differences between practices serving higher-risk and those serving lower-risk patients.

Design: Exploiting the phase-in of the VM on the basis of practice size, regression discontinuity analysis and 2014 Medicare claims were used to estimate differences in practice performance associated with exposure of practices with 100 or more clinicians to full VM incentives (bonuses and penalties) and exposure of practices with 10 or more clinicians to partial incentives (bonuses only). Analyses were repeated with 2015 claims to estimate performance differences associated with a second year of exposure above the threshold of 100 or more clinicians. Performance differences were assessed between practices serving higher- and those serving lower-risk patients after standard Medicare adjustments versus adjustment for additional patient characteristics.

Setting: Fee-for-service Medicare.

Patients: Random 20% sample of beneficiaries.

Measurements: Hospitalization for ambulatory care-sensitive conditions, all-cause 30-day readmissions, Medicare spending, and mortality.

Results: No statistically significant discontinuities were found at the threshold of 10 or more or 100 or more clinicians in the relationship between practice size and performance on quality or spending measures in either year. Adjustment for additional patient characteristics narrowed performance differences by 9.2% to 67.9% between practices in the highest and those in the lowest quartile of Medicaid patients and Hierarchical Condition Category scores.

Limitation: Observational design and administrative data.

Conclusion: The VM was not associated with differences in performance on program measures. Performance differences between practices serving higher- and those serving lower-risk patients were affected considerably by additional adjustments, suggesting a potential for Medicare's pay-for-performance programs to exacerbate health care disparities.

Primary funding source: The Laura and John Arnold Foundation and National Institute on Aging.

Figure 1. Discontinuities in the relationship between practice size and performance associated with practice exposure to the Value-Based Payment Modifier

This figure presents binned scatterplots of claims-based measures used to assess practice performance for the Value-Based Payment Modifier. Each point in the graphs is an average calculated among all patients attributed to practices of a particular size (indicated on the horizontal axis), and is adjusted for the patient characteristics listed in Table 2, indicators of 27 Chronic Conditions Data Warehouse (CCW) chronic conditions, and counts of chronic conditions (Appendix). Fitted values from regression discontinuity models, adjusted for patient characteristics and a linear trend in practice size, are superimposed on the scatterplots (blue lines). The vertical distance between the fitted lines at the 10 and 100 clinician thresholds (dashed vertical lines) correspond to the regression discontinuity estimates reported below the graphs. 95% confidence intervals for the regression discontinuity estimates were calculated using standard errors clustered at the practice level.

Figure 1. Discontinuities in the relationship between practice size and performance associated with practice exposure to the Value-Based Payment Modifier

This figure presents binned scatterplots of claims-based measures used to assess practice performance for the Value-Based Payment Modifier. Each point in the graphs is an average calculated among all patients attributed to practices of a particular size (indicated on the horizontal axis), and is adjusted for the patient characteristics listed in Table 2, indicators of 27 Chronic Conditions Data Warehouse (CCW) chronic conditions, and counts of chronic conditions (Appendix). Fitted values from regression discontinuity models, adjusted for patient characteristics and a linear trend in practice size, are superimposed on the scatterplots (blue lines). The vertical distance between the fitted lines at the 10 and 100 clinician thresholds (dashed vertical lines) correspond to the regression discontinuity estimates reported below the graphs. 95% confidence intervals for the regression discontinuity estimates were calculated using standard errors clustered at the practice level.

Figure 2. Risk-adjusted differences between practices serving patients with higher vs. lower rates of Medicaid enrollment and HCC risk scores, before vs. after adjustment for additional patient characteristics

The graphs show the average performance of the first through fourth quartiles of practices, grouped based on the proportion of patients with dual enrollment in Medicaid or on patients’ on Hierarchical Condition Category (HCC) scores, where practice performance is risk-adjusted: (1) for the base variables used in CMS risk-adjustment methods, and (2) for all patient characteristics listed in Table 1. The proportion of patients dually enrolled in Medicare and Medicaid was 5.3%, 12.7%, 20.9%, and 50.1% in the lowest, second, third, and highest quartiles, respectively. The mean HCC score was 1.00, 1.22, 1.34, and 1.90 in the first, second, third, and fourth quartiles, respectively. For all outcomes, we observed a statistically significant (P<0.001) reduction in performance differences between the highest and lowest quartiles of practices as a result of the additional adjustments (see Appendix for details). For hospitalization for ACSCs, Medicare spending, and mortality, the additional adjustments narrowed differences between the highest and lowest quartile of practices (grouped by patients’ dual eligibility status) by 55.9%, 11.9%, and 34.8%, respectively. The additional adjustments reduced differences between the highest and lowest quartile of practices (grouped by patients’ HCC scores) by 67.9%, 9.2%, and 21.6%, respectively, for hospitalization for ACSCs, Medicare spending, and mortality.

Figure 2. Risk-adjusted differences between practices serving patients with higher vs. lower rates of Medicaid enrollment and HCC risk scores, before vs. after adjustment for additional patient characteristics

The graphs show the average performance of the first through fourth quartiles of practices, grouped based on the proportion of patients with dual enrollment in Medicaid or on patients’ on Hierarchical Condition Category (HCC) scores, where practice performance is risk-adjusted: (1) for the base variables used in CMS risk-adjustment methods, and (2) for all patient characteristics listed in Table 1. The proportion of patients dually enrolled in Medicare and Medicaid was 5.3%, 12.7%, 20.9%, and 50.1% in the lowest, second, third, and highest quartiles, respectively. The mean HCC score was 1.00, 1.22, 1.34, and 1.90 in the first, second, third, and fourth quartiles, respectively. For all outcomes, we observed a statistically significant (P<0.001) reduction in performance differences between the highest and lowest quartiles of practices as a result of the additional adjustments (see Appendix for details). For hospitalization for ACSCs, Medicare spending, and mortality, the additional adjustments narrowed differences between the highest and lowest quartile of practices (grouped by patients’ dual eligibility status) by 55.9%, 11.9%, and 34.8%, respectively. The additional adjustments reduced differences between the highest and lowest quartile of practices (grouped by patients’ HCC scores) by 67.9%, 9.2%, and 21.6%, respectively, for hospitalization for ACSCs, Medicare spending, and mortality.

Figure 3. Simulated changes in practice rankings using base CMS risk adjustment versus adjustment for additional patient characteristics

These graphs summarize practice performance under two risk-adjustment approaches: adjustment for the base variables used in CMS risk-adjustment methods vs. adjustment for additional patient-level factors listed in Table 1. For each outcome, we simulated the proportion of practices whose performance ranking would change by ≥1 decile after additional adjustments. Simulations were based on 10,000 draws from a multivariate normal distribution based on the empirical variances and correlations of practice performance under the two risk-adjustment approaches (Appendix).

Figure 3. Simulated changes in practice rankings using base CMS risk adjustment versus adjustment for additional patient characteristics

These graphs summarize practice performance under two risk-adjustment approaches: adjustment for the base variables used in CMS risk-adjustment methods vs. adjustment for additional patient-level factors listed in Table 1. For each outcome, we simulated the proportion of practices whose performance ranking would change by ≥1 decile after additional adjustments. Simulations were based on 10,000 draws from a multivariate normal distribution based on the empirical variances and correlations of practice performance under the two risk-adjustment approaches (Appendix).