Abstract
The merit-based incentive payment system (MIPS) is a value-based payment model created by the Centers for Medicare & Medicaid Services (CMS) to promote high-value care through performance-based adjustments of Medicare reimbursements. In this cross-sectional study, we examined the participation and performance of oncologists in the 2019 MIPS. Oncologist participation was low (86%) compared to all-specialty participation (97%). After adjusting for practice characteristics, higher MIPS scores were observed among oncologists with alternative payment models (APMs) as their filing source (mean score, 91 for APMs vs. 77.6 for individuals; difference, 13.41 [95% CI, 12.21, 14.6]), indicating the importance of greater organizational resources for participants. Lower scores were associated with greater patient complexity (mean score, 83.4 for highest quintile vs. 84.9 for lowest quintile, difference, −1.43 [95% CI, −2.48, −0.37]), suggesting the need for better risk-adjustment by CMS. Our findings may guide future efforts to improve oncologist engagement in MIPS.
Keywords: Medicare, MIPS, oncology, value-based
With increasing cancer-related healthcare spending, there has been a greater emphasis on payment reforms to incentivize lower costs while maintaining high-quality care in oncology. This article focuses on provider- and practice-level factors associated with oncologist participation and performance in MIPS, the payment system used by the Centers for Medicare and Medicaid Services.
Introduction
With steadily increasing cancer-related healthcare spending, there has been a greater emphasis on payment reforms to incentivize lower costs while maintaining high-quality care in oncology.1,2 All physicians accepting Medicare are subject to quality performance review through either the merit-based incentive payment system (MIPS) or advanced alternative payment models (APMs). In MIPS, clinicians earn a yearly composite score based on their performance in 4 domains: quality, cost, improvement activities, and promoting interoperability. This merit-based score is used by CMS to adjust reimbursements. In advanced APMs (eg, oncology care model, now closed; or enhancing oncology model, set to begin in 2023), participants take on more financial risk than in MIPS, but can earn more rewards for delivering high-quality care. While clinicians participating in advanced APMs are exempt from MIPS participation, members of some “non-advanced” APMs may participate in MIPS under specialized agreements with CMS.
Prior studies have explored the impact of MIPS on several specialties, including surgery and dermatology.3,4 Yet, little information exists about participation or performance in MIPS among oncologists. This is especially important as oncologists commonly report being unprepared to engage in value-based payment models due to unfamiliarity.1,5 In this analysis, we identified provider- and practice-level factors associated with oncologist participation and performance in MIPS.
Methods
We obtained data on provider demographics and practice characteristics from the 2019 doctors and clinicians file and provider utilization datasets, as previously described.3,4,6 Each database includes all US physicians who submitted at least one Medicare claim in 2019. We linked these data to the MIPS performance files6 using national provider identifiers to identify physicians who participated in the 2019 MIPS performance year, which determines 2021 payment adjustments. From the MIPS performance files, we extracted MIPS composite scores and category scores for each participant. Our cohort was limited to physicians with a reported primary specialty of medical, hematology-, radiation, surgical, or gynecological oncology. Physicians were deemed MIPS-exempt if they met the low volume threshold, defined as billing <$90 000 for Part B services and seeing <200 beneficiaries annually. Dual eligibility for Medicare and Medicaid was used as a proxy for socioeconomic risk and Hierarchical Condition Category (HCC) scores were used to estimate clinical complexity; both are physician-level measures of case mix.
We compared the characteristics of participating and non-participating oncologists and assessed the differences between these groups using chi-squared tests for binary and categorical variables and t-tests for continuous variables. Because MIPS scores range between 0 and 100, we used multivariable Tobit regression (lower limit, 0; upper limit, 100) to identify factors associated with MIPS performance. Statistical testing was 2-sided (α = .05). Analyses were conducted using MATLAB 2021b (MathWorks). This de-identified, public database study was deemed exempt from Institutional Review Board review.
Results
Of 16 511 eligible oncologists, 14 211 (86%) participated in MIPS. Of 4620 non-participants, 2320 (50%) met the low volume threshold and were MIPS-exempt while 2300 (50%) were elective non-participants (Table 1). Most participants were male (67.3%) and filed using an APM (52.6%). Compared to non-participants, participants were more likely to have fewer mean (SD) beneficiaries (293.6 [207.7] vs. 370.2 [162.6]; P < .001), be hematologist oncologists (6549 [46.1%] vs. 1,328 [57.7%]; P < .001), and be affiliated with NCI-designated cancer centers (3,019 [21.2%] vs. 206 [9%]; P < .001).
Table 1.
Characteristics of oncologists participating in MIPS.
| Variable | Participants | Elective non-participants |
P-valuea |
|---|---|---|---|
| Eligible oncologistsb (N =16511) | 14211 (86.1) | 2300 (13.9) | |
| Sex, no. (%) | |||
| Female | 4640 (32.7) | 735 (32) | .51 |
| Male | 9571 (67.3) | 1565 (68) | |
| Years in practicec, mean (SD) | 23.7 (11.7) | 23.9 (11.1) | <.001 |
| Specialty, no. (%) | |||
| Gynecological oncology | 744 (5.2) | 76 (3.3) | <.001 |
| Hematology oncology | 6549 (46.1) | 1328 (57.7) | |
| Medical oncology | 2736 (19.3) | 342 (14.9) | |
| Radiation oncology | 3442 (24.2) | 476 (20.7) | |
| Surgical oncology | 740 (5.2) | 78 (3.4) | |
| Credentials, no. (%) | |||
| Allopathic | 13632 (95.9) | 2168 (94.3) | <.001 |
| Osteopathic | 579 (4.1) | 132 (5.7) | |
| No. of beneficiaries, mean (SD) | 293.6 (207.7) | 370.2 (162.6) | <.001 |
| Mean beneficiary age, y mean (SD) | 72.7 (2.6) | 73.3 (1.8) | <.001 |
| Proportion of dual eligible beneficiaries, mean (SD) | 19.1 (12.6) | 16.8 (11.2) | <.001 |
| Beneficiary HCC risk scored, mean (SD) | 2.1 (0.5) | 2 (0.4) | < .001 |
| Filing source, no. (%) | |||
| Alternate payment model | 7478 (52.6) | NA | NA |
| Group | 5829 (41) | NA | |
| Individual | 904 (6.4) | NA | |
| Practice size, no. (%) | |||
| 1-9 | 1707 (12.3) | 244 (10.9) | <.001 |
| 10-99 | 3464 (24.9) | 499 (22.2) | |
| ≥100 | 8732 (62.8) | 1501 (66.9) | |
| Region, no. (%) | |||
| Midwest | 2876 (20.2) | 660 (28.7) | <.001 |
| Northeast | 3287 (23.1) | 471 (20.5) | |
| South | 5231 (36.8) | 578 (25.1) | |
| West | 2817 (19.8) | 591 (25.7) | |
| Rurality, no. (%) | |||
| Nonrural | 12828 (90.3) | 2099 (91.3) | .28 |
| Rural | 1381 (9.7) | 201 (8.7) | |
| Hospital affiliation, no. (%) | |||
| NCI-designated | 3019 (21.2) | 206 (9) | <.001 |
| Other | 11192 (78.8) | 2094 (91) | |
a P-values for comparison of participants and elective non-participants were obtained using χ2 tests for binary and categorical variables and t-tests for continuous variables. All statistical tests were 2-sided. Significance was defined as P < .05.
bMIPS-eligible oncologists included those who did not meet the low-volume threshold in the 12-month determination period, which includes billing more than $90 000 for more than 200 Part B covered professional services and seeing more than 200 beneficiaries. The low-volume threshold for APM participants is determined at the APM entity level.
cYears since medical school graduation.
dPatient HCC risk scores were obtained from the CMS risk-adjustment model based on both disease and demographic-related factors. A score of 1 represents a patient with average clinical risk and higher scores correspond to greater risk.
Abbreviations: HCC, hierarchical condition categories; NCI, National Cancer Institute.
Oncologists had a mean (SD) MIPS score of 86.7 (13.2); 13 952 (98.2%) received a net positive payment adjustment. The mean (SD) all-specialty MIPS score was 86.1 (17.5) (Supplementary Table S1). In the multivariable analysis, oncologists filing as groups (−7.18; 95% CI, [−7.71, −6.66]) or individuals (−13.41; [−14.6, −12.21]) had lower MIPS scores than those filing as APMs (Table 2). Oncologists who treated patients with greater clinical complexity (−1.43; [−2.48, −0.37]), were used at NCI-designated hospitals (−1.58; [−2.28, −0.87]), or practiced in the South (−4.04; [-−.76, −3.33]) also had lower adjusted MIPS scores. Better MIPS performance was associated with larger practices (2.17; [1.29, 3.05]). In a sub-analysis of MIPS category scores, the association of group and individual filing with lower composite MIPS scores was driven by lower quality performance; the association of clinical complexity with lower composite MIPS scores was driven by lower quality and lower cost performance (Supplementary Table S2).
Table 2.
Association of final MIPS score with oncologist- and practice-level characteristics. Adjusted MIPS scores using marginal standardization are reported.
| Characteristic | Adjusted MIPS score | Difference (95% CI) | SE | P-valuea |
|---|---|---|---|---|
| Sex | ||||
| Female | 84.2 | 0.09 (-0.5 to 0.7) | 0.29 | .77 |
| Male | 84.1 | [Reference] | NA | NA |
| Years in practiceb | ||||
| 1-9 | 85 | [Reference] | NA | NA |
| 10-24 | 84 | −1.02 (−1.93 to −0.11) | 0.46 | .03 |
| ≥25 | 83.5 | −1.48 (−2.41 to −0.55) | 0.47 | <.001 |
| Specialty | ||||
| Gynecological oncology | 83.9 | 0.51 (−0.79 to 1.8) | 0.66 | .44 |
| Hematology oncology | 83.4 | [Reference] | NA | NA |
| Medical oncology | 83.7 | 0.24 (−0.49 to 0.97) | 0.37 | 0.52 |
| Radiation oncology | 84.6 | 1.17 (0.42 to 1.91) | 0.38 | <.001 |
| Surgical oncology | 85.3 | 1.86 (0.57 to 3.15) | 0.66 | <.001 |
| Credential | ||||
| Allopathic | 84.8 | [Reference] | NA | NA |
| Osteopathic | 83.5 | −1.27 (−2.57 to 0.02) | 0.66 | .05 |
| Beneficiaries, no. | ||||
| 11-49 | 84.6 | [Reference] | NA | NA |
| 50-499 | 84.2 | −0.37 (−2.55 to 1.81) | 1.11 | .74 |
| ≥500 | 83.7 | −0.86 (−3.15 to 1.42) | 1.17 | .46 |
| Beneficiary age, years | ||||
| 22-70 | 84.5 | [Reference] | NA | NA |
| 71-73 | 84.1 | −0.43 (−1.31 to 0.45) | 0.45 | .34 |
| ≥74 | 83.8 | −0.7 (−1.69 to 0.29) | 0.51 | .17 |
| Dual eligible caseloadc | ||||
| Low | 83.4 | [Reference] | NA | NA |
| Moderate | 84.3 | 0.9 (0.2 to 1.6) | 0.36 | .01 |
| High | 84.8 | 1.35 (0.43 to 2.28) | 0.47 | <.001 |
| Beneficiary HCC risk scored | ||||
| Low | 84.9 | [Reference] | NA | NA |
| Moderate | 84.2 | −0.64 (−1.43 to 0.151) | 0.4 | .11 |
| High | 83.4 | −1.43 (−2.48 to −0.37) | 0.54 | .01 |
| Filing source | ||||
| Alternate payment model | 91 | [Reference] | NA | NA |
| Group | 83.8 | −7.18 (−7.71 to -6.66) | 0.27 | <.001 |
| Individual | 77.6 | −13.41 (−14.6 to −12.21) | 0.61 | <.001 |
| Group size | ||||
| 1-9 | 83.4 | [Reference] | NA | NA |
| 10-99 | 83.6 | 0.27 (−0.66 to 1.19) | 0.47 | .57 |
| ≥100 | 85.5 | 2.17 (1.29 to 3.05) | 0.45 | <.001 |
| Region | ||||
| Midwest | 86.5 | [Reference] | NA | NA |
| Northeast | 84.4 | −2.09 (−2.88 to −1.3) | 0.4 | <.001 |
| South | 82.5 | −4.04 (−4.76 to -3.33) | 0.36 | <.001 |
| West | 83.3 | −3.19 (−4.04 to -2.35) | 0.43 | <.001 |
| Rurality | ||||
| Nonrural | 83.4 | [Reference] | NA | NA |
| Rural | 84.9 | 1.46 (0.56 to 2.36) | 0.46 | <.001 |
| Hospital affiliation | ||||
| NCI-designated | 83.4 | −1.58 (−2.28 to -0.87) | 0.36 | <.001 |
| Other | 85 | [Reference] | NA | NA |
aSignificance was defined as P < .05.
bYears since medical school graduation.
cBottom (low) vs. middle 3 (moderate) vs. top (high) quintiles of clinicians for proportion of dually eligibility patients treated in 2019.
dBottom (low) vs. middle 3 (moderate) vs. top (high) quintiles of clinicians for HCC risk score of patients treated in 2019.
Abbreviations: HCC, hierarchical condition categories; SE, standard error; NCI, National Cancer Institute.
Discussion
Oncologist participation in MIPS (86%) was well below national participation among all specialties (97%).7 This may be explained by low preparedness for MIPS or inconsistent electronic health record interoperability among oncology practices.8 Per CMS, continued non-participation will result in a −9% payment adjustment beginning with the 2022 performance year, which may incentivize future participation.9 While most oncologists received a positive payment adjustment, the minimum score to avoid a penalty will be raised from 30 to 75 in 2022. Further, while the 2019 MIPS composite score comprised 45% quality measures and 15% cost measures, these weights are set to be equalized starting in 2022, which may disproportionately impact high-cost specialties like oncology. Together, these changes may result in lower reimbursement in the future for oncologic practices.
Greater clinical complexity was associated with lower MIPS scores, owing to lower quality and cost performance. It is unclear whether patients with greater comorbidities are receiving less optimal care or whether current measures are simply failing to capture the complexity and excess cost of delivering oncologic care to these patients. Regardless, better risk adjustment by CMS, perhaps through specialty-specific recalibration of the current complex patient bonus, is needed to ensure that oncologists are not penalized for providing standard of care treatment, often in clinically complex environments.2 Another avenue may be the adoption of MIPS value pathways, which offer specialty-specific measures and activities that may more accurately vary in specialty-specific performance; although the oncology-specific pathway is pending approval.
Higher MIPS scores were associated with participation in APMs, owing to better quality performance. This was likely driven by an emphasis on quality metric reporting for practices that take on additional financial risk and the exemption of cost scoring for APMs.10 Similarly, larger practices were found to have higher MIPS scores, a concerning trend for smaller practices and individual providers during times of greater practice consolidation. As lack of knowledge and resources are known barriers to MIPS engagement among oncologists, clinicians without access to APMs or greater organizational resources dedicated to MIPS should consider familiarizing themselves with MIPS reporting to gain insight into measure selection and process improvements.1
Our findings reveal suboptimal MIPS participation among oncologists. Efforts to promote greater engagement and more equitable MIPS scoring for oncologists are needed to prevent unfavorable changes in their future reimbursement. Limitations of this analysis include unavailable data on payment adjustments, site of care, or specific scoring metrics driving performance.
Supplementary Material
Contributor Information
Vishal R Patel, Dell Medical School, The University of Texas at Austin, Austin, TX, USA.
Thomas B Cwalina, Case Western Reserve University School of Medicine, Cleveland, OH, USA.
Arjun Gupta, Division of Hematology, Oncology and Transplantation, Department of Medicine, Masonic Cancer Center, University of Minnesota, MN, USA.
Nico Nortjé, Section of Clinical Ethics, Department of Critical Care Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Samyukta Mullangi, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Ravi B Parikh, Penn Center for Cancer Care Innovation, Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, USA; Department of Medicine, Perelman School of Medicine, University of Pennsylvania, PA, USA.
Ya-Chen Tina Shih, Section of Cancer Economics and Policy, Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
S M Qasim Hussaini, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA.
Funding
The authors have no funding sources to declare.
Conflict of Interest
The authors indicated no financial relationships.
Author Contributions
Conception/design: V.R.P., T.B.C., Y.-C.T.S., S.M.Q.H. Provision of study material or patients: V.R.P. Collection and/or assembly of data: V.R.P. Data analysis and interpretation: V.R.P., T.B.C., A.G., Y.-C.T.S., S.M.Q.H. Manuscript writing: All authors. Final approval of manuscript: All authors.
Data Availability
The data underlying this article will be shared on reasonable request to the corresponding author.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data underlying this article will be shared on reasonable request to the corresponding author.