Association Between Urologist Merit-Based Incentive Payment System Performance and Quality of Prostate Cancer Care

Abstract

Introduction: We performed a study to evaluate the association between urologist performance in the Merit-Based Incentive Payment System (MIPS), and quality and spending for prostate cancer care.

Methods: Medicare beneficiaries with prostate cancer diagnosed between 2017 and 2019 were assigned to their primary urologist. Associated MIPS scores were identified and categorized based on thresholds for payment adjustment as low (worst), moderate, and high (best). Multivariable mixed effects models were used to measure the association between MIPS performance and adherence to quality measures and price standardized spending for prostate cancer.

Results: Adherence to quality measures did not vary across MIPS performance groups for pretreatment counselling by both a urologist and radiation oncologist (low-76%, [95% CI 73%-80%], moderate-77% [95% CI 74%-79%], and high-75% [95% CI 74%-76%]) and avoiding treatment in men with a high risk of noncancer mortality within 10 years of diagnosis (low-40% [95% CI 35%-45%], moderate-39% [95% CI 36%-43%], high-38% [95% CI 36%-39%]). Men on active surveillance managed by high performers more likely received a confirmatory test (44% [95% CI 43%-46%]) compared to those managed by moderate (38% [95% CI 33%-42%]) performers, but not low performers (36% [95% CI 29%-44%]). There was no difference in adjusted spending across MIPS performance groups.

Conclusions: Better performance in MIPS is associated with a higher rate of confirmatory testing in men initiating active surveillance for prostate cancer. However, performance was not associated with other dimensions of quality nor spending.

Keywords: Medicare; prostate cancer; quality; value-based care.

Figure 1.

Adherence to quality measures across MIPS performance groups for A) received pretreatment counselling by a urologist and a radiation oncologist, B) received a confirmatory test for men on active surveillance, and C) avoidance of treatment for men with high risk of non-cancer mortality. Models adjusted for age, comorbidity, socioeconomic status, race, rural residence, practice organization, urologist density, radiation oncologist density, number of hospital beds per 100K beneficiaries, and Medicare advantage penetration.

Figure 1.

Adherence to quality measures across MIPS performance groups for A) received pretreatment counselling by a urologist and a radiation oncologist, B) received a confirmatory test for men on active surveillance, and C) avoidance of treatment for men with high risk of non-cancer mortality. Models adjusted for age, comorbidity, socioeconomic status, race, rural residence, practice organization, urologist density, radiation oncologist density, number of hospital beds per 100K beneficiaries, and Medicare advantage penetration.

Figure 1.

Adherence to quality measures across MIPS performance groups for A) received pretreatment counselling by a urologist and a radiation oncologist, B) received a confirmatory test for men on active surveillance, and C) avoidance of treatment for men with high risk of non-cancer mortality. Models adjusted for age, comorbidity, socioeconomic status, race, rural residence, practice organization, urologist density, radiation oncologist density, number of hospital beds per 100K beneficiaries, and Medicare advantage penetration.

Figure 2.

Adjusted spending per beneficiary among A) entire cohort and B) those who received treatment, stratified by urologist MIPS performance. Models adjusted for age, comorbidity, socioeconomic status, race, rural residence, practice organization, urologist density, radiation oncologist density, number of hospital beds per 100K beneficiaries, and Medicare advantage penetration.

Figure 2.

Adjusted spending per beneficiary among A) entire cohort and B) those who received treatment, stratified by urologist MIPS performance. Models adjusted for age, comorbidity, socioeconomic status, race, rural residence, practice organization, urologist density, radiation oncologist density, number of hospital beds per 100K beneficiaries, and Medicare advantage penetration.