Ahmed Shelbaya, MD

 To quantify the impact of biosimilar adoption on oncology provider financial risk under the Oncology Care Model (OCM) using real-world data.

 Retrospective study of Medicare fee-for-service beneficiaries potentially eligible for an OCM episode of care initiating between January 2019 and June 2021.

 Cancer treatment episodes were identified from the Medicare 5% Limited Data Set (LDS). The study sample consisted of episodes with use of any of the following agents or their biosimilars: bevacizumab, rituximab, trastuzumab, epoetin alfa, filgrastim, and pegfilgrastim. Financial risk was defined as the difference in observed total cost of care vs OCM benchmark episode cost. The primary outcome was the difference in financial risk (expressed in nominal US$) under observed use of reference agents and/or biosimilars vs hypothetical use of 100% reference product.

 The sample included 8851 episodes. Biosimilar adoption resulted in a mean cost reduction of $1023 per 6-month episode vs hypothetical 100% use of reference agents. Biosimilar use increased markedly over the study horizon—with use in 65% of episodes initiating in half 1 (H1) of 2021. The mean cost of care reduction in episodes initiating in H1 2021 ($2060; 4.1% of mean episode benchmark price) improved nearly 10-fold vs H1 2019 ($201; 0.4% of mean episode benchmark price).

 Rapid adoption of biosimilars for Medicare beneficiaries included in the LDS led to substantial cost savings for cancer episodes evaluated under the OCM methods—suggesting that adoption of biosimilars is a key strategy that providers should consider to manage financial risk in value-based payment models.

We estimate that adoption of oncology biosimilars in Medicare value-based payment (VBP) models resulted in cost savings of $1023 per 6-month episode of care (2.07% reduction vs mean episode benchmark).

A biosimilar adoption strategy is important to reduce cost and mitigate provider risk in 2-sided oncology VBP models.

We believe this is the first study to apply biosimilar real-world data to oncology VBP models to quantify the incremental cost and performance impacts.

Our findings inform policy decisions regarding cost of care and provider performance impacts of biosimilars and highlight priority cancers and biologics to maximize impact.

medicare | Image credit: japhoto - stock.adobe.com

In response to the rising cost of cancer care in the US, policy makers and health care stakeholders have increasingly turned to value-based payment (VBP) models as a potential solution.

 Broadly, these models are designed to align reimbursement with patient outcomes and the overall value of care, and they have shown promise in both controlling costs and enhancing the quality of oncological care.

 However, the translation of these models into widespread practice requires careful consideration of structural, clinical, and financial challenges and must be supported by ongoing research and policy refinement.

Biosimilars represent a promising group of drugs within the context of VBPs, as they meet high standards for comparability to existing approved biological products (or 

) in terms of structure, quality, safety, and efficacy and typically have an average sales price (ASP) more than 25% lower than reference products.

 Additionally, biosimilars are highly prevalent in oncology indications, with FDA-approved biosimilars for 8 molecules in oncology as of April 2026.

 As such, biosimilars could be critical tools to help providers manage VBP-related financial risk while still delivering high-quality care.

The objective of this study was to quantify the impact of biosimilar adoption on total cost of care and oncology provider financial risk in VBP models using real-world data. To provide a concrete, quantitative underpinning to our evaluation, we employed the methodology of Medicare’s oncology VBP models as an exemplar. Medicare has been a leader in the oncology VBP space, having launched 2 of the largest oncology VBP models in the US to date: most recently, the Enhancing Oncology Model (EOM), which launched in July 2023 and reopened for new participants from May to September 2024, and the Oncology Care Model (OCM), which ran from July 2016 to June 2022, with final evaluation of the program published in May 2024.

 Broadly, in these programs, provider cost of care is compared to benchmarks calculated from retrospective analyses of patients treated outside the context of a VBP. Providers exceeding cost benchmarks can owe a recoupment to CMS, and those below cost benchmarks are eligible for performance-based (additional) payments. Although key parameters of the EOM payment methodology are still uncertain, the OCM and EOM have significant overlap in their episode identification, cost, and benchmark pricing methodologies, allowing us to use available data and the fully finalized OCM methodology to provide timely and actionable information to providers.

This study takes the perspective of cancer care providers facing choices between use of biosimilars or their reference products in the context of VBP models. Our findings can help these providers understand the impacts of choosing biosimilars in this context on total cost of care and performing favorably in VBP models. Our findings may be particularly beneficial for providers who are new to value-based care arrangements and elected to participate in the second EOM cohort, which began in July 2025.

The Medicare 5% Limited Data Set (LDS) analytic files from 2019 to 2021 were used to evaluate the impact of biosimilar adoption on provider risk and financial performance in VBP models in the context of the OCM payment methodology.

 We used LDS data from 2016 to 2021 to implement required components of the OCM methodology.

Prices for biologic reference products and biosimilars were taken from the Medicare ASP Drug Pricing File

 from the year and quarter indicated by the date of service on the claim for the product.

Biosimilars that met the following criteria were selected: (1) biologic products designated by the FDA as biosimilar to a corresponding reference product and commercially available prior to December 31, 2021 (the last possible date for claims in episodes included in OCM performance period [PP] 10)

; (2) biosimilar products with an FDA indication related to at least 1 of the 21 OCM-eligible cancers

; and (3) assigned an individual Healthcare Common Procedure Coding System (HCPCS) code no later than the October 2021 CMS quarterly HCPCS code update.

 The following 6 sets of biologic products met our inclusion criteria: bevacizumab (Avastin) biosimilars,

The unit of analysis was a 6-month patient episode of care as defined by the OCM methodology, which included episode triggers, episode termination criteria, cancer assignment, and overall OCM inclusion/exclusion criteria.

The primary outcome of the study was the cost impact of observed biosimilar use relative to a counterfactual with the corresponding reference product. The primary outcome was defined as the difference in episode total cost of care (TCOC) with the observed use of reference and biosimilar products compared with TCOC with use of reference products only. Methods for calculating the OCM TCOC benchmark are explained in detail in the 

The study used the OCM simulation module in the Tuple Health RWE Technology Platform, which operationalizes all key facets of the OCM methodology including OCM TCOC calculation, episode framing, cancer attribution, risk adjustment, and the OCM’s inclusion/exclusion criteria.

 Tuple Health’s OCM simulation module was previously cross-validated using empirical OCM data, independent of this study. Modeled predictions for OCM TCOC targets were greater than 99.9% accurate compared with the episode targets provided by Medicare.

This core OCM methodology implementation was extended to include simulated Part D costs and Part D enrollment for OCM episodes to address key gaps in publicly available research data sets.

 Part D cost ranges were initially estimated from a random sample of OCM data, and zero-inflated γ distributions for sampling were assumed to construct the models.

From the initial set of episodes with patterns of care that qualified for inclusion in the OCM, we identified all episodes utilizing reference products corresponding to the biosimilars of interest and any episodes from the LDS data using one of the study biosimilars. We referred to these as 

 (BAEs). These reference products and their corresponding biosimilars were considered 

Episodes in the study population initiated between January 2, 2019, and July 1, 2021, corresponding to OCM PP 6 to PP 10. For each episode in the study population, we computed the TCOC and performed episode cost truncation (winsorization) in accordance with the OCM methodology. Costs for each BAE were computed first under the observed use of biosimilar and reference products, then again under the assumption of the use of reference products only. Costs for BAPs were calculated using the number of billed units in the episode (inclusive of drug waste) for each BAP multiplied by the per-unit prices for the product taken from the Medicare ASP Drug Pricing File from the year and quarter corresponding to the service date of the claim associated with the use of the product.

Of the 3,641,034 beneficiaries present in the 2019-2021 LDS data, 24,845 beneficiaries met the conditions (eg, cancer type, treatment type, and lack of COVID-19 diagnosis, among others) for triggering a potentially eligible VBP episode of care, with a total of 39,571 potentially eligible episodes of care identified (Table 1). Of those potentially reconciliation-eligible episodes of care, 8851 were determined to be BAEs (see eAppendix for additional product-specific details). Aggregated across the entire study period, 55.3% of BAEs had use of reference BAPs only. The observed use of biosimilar BAPs resulted in cost reductions of $1023 per 6-month episode compared with the hypothetical use of only reference products, corresponding to a cost reduction equivalent to 2.1% of the mean episode benchmark price.

, biosimilar use increased steadily over the study period. Among episodes initiating in half 1 (H1) of 2019, 24.3% had use of a biosimilar. By H1 2021, 65% of BAEs had use of a biosimilar. This increased use was primarily driven by biosimilar antineoplastics (1.4% of episodes in H1 2019 to 36.4% in H1 2021) but was also due to the adoption of supportive care biosimilar agents (23.0% in H1 2019 to 34.1% in H1 2021). Mean financial risk reduction improved nearly 10-fold with the rapid adoption of biosimilars over this period, from $201 per episode (representing a cost reduction equivalent to 0.4% of the mean benchmark price) in H1 2019 to $2060 (4.1% of mean benchmark price) in H1 2021.

 compares the rates of biosimilar adoption and resulting financial impact by product class (antineoplastic vs supportive therapies). Averaged over the study period, adoption of biosimilar antineoplastics led to a mean financial risk reduction of $880 per episode. Risk reduction grew rapidly in each period, from $14 in H1 2019 to $2077 in H1 2021. Adoption of biosimilar supportive therapies led to a mean financial risk reduction of $142 per episode. The use of biosimilar supportive therapies led to risk reduction in the first 4 periods but resulted in a risk increase of $22 in the final period, due primarily to significant decreases in the reimbursement costs for reference pegfilgrastim over the study period.

, the 5 most prevalent cancer types assigned to BAEs were breast (19.8%), lymphoma (19.1%), small intestine/colorectal (12.2%), lung (12.0%), and prostate (6.8%). On a per-episode basis, the impact of biosimilar use was most cost saving in renal cancer ($2487), central nervous system (CNS) tumors ($1998), small intestine/colorectal cancer ($1721), lymphoma ($1523), and chronic leukemia ($1407). On a per-episode basis, the impact of biosimilar substitution was least cost saving in endocrine tumors ($54), prostate cancer ($148), pancreatic cancer ($227), bladder cancer ($267), and head and neck cancer ($309). Biosimilar use was more beneficial in cancers included in the EOM ($1085 per episode) than in non-EOM cancers ($831).

In our study, we assessed the impact of biosimilar adoption on provider risk and financial performance in VBP models by applying the Medicare OCM methodology to Medicare fee-for-service claims in the 2019-2021 period. We found that rapid adoption of biosimilars led to substantial TCOC savings and reduction in provider financial risk for exceeding VBP model cost of care benchmarks. Evaluated under the methodology of the OCM, biosimilar use resulted in a mean cost reduction of $1023 per episode relative to a counterfactual with only reference products. Due to declining biosimilar price and increased utilization, the mean cost reduction increased by a factor of 10 in H1 2021 vs H1 2019 ($2060 per episode vs $201 per episode). We found that the overall impact of biosimilar adoption was driven by increases in the use of biosimilar antineoplastics, from 1.4% of episodes in H1 2019 to 36.4% in H1 2021. Over this time, mean financial risk reduction attributed to biosimilar antineoplastic use increased from $14 to $2077 per episode. In subgroup analyses, we found that cancer types for which monoclonal antibody therapies are typically given in larger doses or for longer durations (eg, in maintenance therapy) exhibited even greater savings (breast cancer, small intestine/colorectal cancer, lymphoma, chronic leukemia, ovarian cancer, liver cancer, CNS tumors, and kidney cancer).

When we similarly applied our analytic approach to episodes with cancer types included in the newer Medicare EOM, we found that the included cancer types had particularly high uptake of biosimilars—especially biosimilar antineoplastics. We found that BAEs assigned to a cancer type included in the EOM (75.5% of all BAEs) had disproportionately high use of antineoplastic BAPs (representing 84.4% of BAEs with use of a BAP antineoplastic). For this reason, we expect the cost impact of biosimilar substitution to be particularly pronounced in EOM-included cancers. If biosimilar adoption remains at the level observed in our study population in H1 2021, we estimate that total episode costs across EOM episodes would be approximately 1.3% lower ($748) than they would be without the use of any biosimilars (eAppendix). This could result in providers needing to meet lower benchmark prices or represent an opportunity to more readily meet incentive thresholds. Further, this is likely an underestimate of the cost impact of biosimilars for episodes triggering after new provider enrollment into the EOM. Beginning in 2025, the EOM definition of total cost of care for Part D agents began reflecting Medicare’s reduced responsibility (from 80% of gross drug cost above the catastrophic threshold to 40% for generic drugs and 20% for brand-name drugs) as required by the Inflation Reduction Act. The net effect of this change will be a reduction in expected episode cost (and thus EOM cost benchmarks), increasing the proportion of episode cost that could be saved via biosimilar substitution.

VBP models can theoretically align the economic incentives of health care payers including Medicare with the medical practices of oncologists—achieving high-quality outcomes at a lower cost of care. However, the highly protocolized nature of modern cancer diagnosis, treatment, and monitoring leaves few opportunities for oncologists to bend the cost curve without sacrificing standard components of testing, imaging, therapy, and/or follow-up that can have implications for quality of care. As such, substitution of biosimilar antineoplastic and supportive care therapies for their more costly reference products represents a uniquely feasible strategy to optimize provider performance in the context of VBP. Over time, it is possible that increased biosimilar use could function to decrease VBP price benchmarks, which could make it more difficult for providers to earn performance-based payments. Nonetheless, choosing biosimilars in this context would still benefit providers because they would be expected to have a lower likelihood of owing recoupments for exceeding cost benchmarks.

This study has several limitations that should be noted. Although we applied the detailed OCM methods to Medicare LDS records during the period when the OCM was conducted, we did not limit our analysis to patients who received care from an OCM participant. As a result, our analysis assumes that the average TCOC effects of biosimilar substitution observed in the broader set of included Medicare patients are representative of the OCM cohort. We believe that this is a reasonable assumption because analysis of OCM PPs 1 through 11 (H2 2016-H2 2021) found that, although statistically significant, the difference in total episode payments between OCM and non-OCM practices was quite small relative to episode cost.

 Additionally, we focused our analysis on the subset of patients eligible for treatment in the OCM with the potential to be treated with biosimilars (BAEs). As such, the average cost impacts of biosimilar substitution would be lessened if averaged over the entire OCM-eligible population in a manner analogous to the analysis reported by Keating et al.

 Finally, our analysis assesses observed cost of care in patients with BAEs vs a hypothetical counterfactual where only reference products are used. This analysis demonstrates the maximum degree to which observed levels of biosimilar uptake can decrease TCOC. If a provider’s baseline use of biosimilars is higher, potential for biosimilar-attributable savings may be diminished, but the benefit of reduced risk of owing recoupments will remain. Although OCM methods for cost impact estimation in the EOM are likely valid, the accuracy of our estimates would be improved with the necessary parameters to conduct EOM benchmarking.

From 2019 to 2021, rapid adoption of biosimilars for Medicare fee-for-service beneficiaries included in the LDS led to substantial cost savings and reduction in financial risk for cancer episodes evaluated under the VBP methods. Broad adoption of biosimilars is a key strategy that providers in oncology VBP models should consider for managing TCOC and optimizing performance.

 Tuple Health (BC, AY, LT), Washington, DC; Pfizer Inc (AS, JAR), New York, NY; Mailman School of Public Health, Columbia University (AS), New York, NY; Comparative Health Outcomes, Policy, and Economics Institute, School of Pharmacy, University of Washington (JAR), Seattle, WA.

 Dr Chaudhry, Dr Yue, and Ms Tran are employees of Tuple Health, which was a paid consultant to Pfizer in connection with the development of this manuscript. Dr Yue also served on an advisory board for Johnson & Johnson in December 2024, which was a roundtable hosted by 

 on value-based care agreements and reimbursement models. Drs Shelbaya and Roth are employees and stockholders of Pfizer.

 Concept and design (BC, AY, AS, JAR); acquisition of data (BC, AY); analysis and interpretation of data (BC, AY, AS, JAR); drafting of the manuscript (BC, AY, AS, LT, JAR); critical revision of the manuscript for important intellectual content (BC, AY, AS, JAR); statistical analysis (BC, AY, JAR); obtaining funding (BC, LT, JAR); administrative, technical, or logistic support (LT); and supervision (BC, JAR).

 Basit Chaudhry, MD, PhD, Tuple Health, 4800 Hampden Ln, Ste 200, Bethesda, MD 20814. Email: basitchaudhry@tuplehealth.com.

1. Promoting value, affordability, and innovation in cancer drug treatment. President’s Cancer Panel. March 2018. Accessed August 13, 2024. 

https://web.archive.org/web/20240802081004/https://prescancerpanel.cancer.gov/report/drugvalue/index.html

2. The current cancer care landscape: an imperative for change. In: Levit L, Balogh E, Nass S, Ganz PA, eds. 

Delivering High-Quality Cancer Care: Charting a New Course for a System in Crisis

. National Academies Press; 2013. Accessed August 13, 2024. 

https://www.ncbi.nlm.nih.gov/books/NBK202145/

3. Cox JV, Sprandio JD, Barkley R. Understanding and surviving the transition to value-based oncology. 

. 2013;33(1):e361-e368. doi:10.14694/EdBook_AM.2013.33.e361

4. Aviki EM, Schleicher SM, Mullangi S. The Oncology Care Model and other value-based payment models in cancer care. 

. 2019;5(3):298-299. doi:10.1001/jamaoncol.2018.5735

Redefining Health Care: Creating Value-Based Competition on Results

6. Value-based payment as a tool to address excess US health spending. 

 research brief. December 1, 2022. Accessed August 13, 2024. 

https://www.healthaffairs.org/do/10.1377/hpb20221014.526546/full/

7. Lyss AJ, Supalla SN, Schleicher SM. The Oncology Care Model—why it works and why it could work better: accounting for novel therapies in value-based payment. 

. 2020;6(8):1161-1162. doi:10.1001/jamaoncol.2019.4385

8. Seymour EK, de Souza JA, Fendrick AM. Incorporating value-based care into oncology. 

. 2020;26(4):311-322. doi:10.1097/PPO.0000000000000459

9. de Souza JA, Ratain MJ, Fendrick AM. Value-based insurance design: aligning incentives, benefits, and evidence in oncology. 

. 2012;10(1):18-23. doi:10.6004/jnccn.2012.0005

Biosimilars in the United States 2023-2027: Competition, Savings, and Sustainability

. IQVIA Institute; January 31, 2023. Accessed August 13, 2024. 

https://www.iqvia.com/insights/the-iqvia-institute/reports-and-publications/reports/biosimilars-in-the-united-states-2023-2027

11. Overview for health care professionals. FDA. Updated August 1, 2024. Accessed August 13, 2024. 

https://www.fda.gov/drugs/biosimilars/overview-health-care-professionals

12. Biosimilar product information. FDA. Accessed August 13, 2024. 

https://www.fda.gov/drugs/biosimilars/biosimilar-product-information

13. Arad N, Rapista D, Lopez MH, McClellan M. Originator biologics and biosimilars: payment policy solutions to increase price competition while maintaining market sustainability in Medicare Part B. Duke Margolis Institute for Health Policy. October 15, 2021. Updated November 17, 2021. Accessed August 13, 2024. 

https://healthpolicy.duke.edu/publications/originator-biologics-and-biosimilars-payment-policy-solutions-increase-price

14. Patel KB, Arantes LH Jr, Tang WY, Fung S. The role of biosimilars in value-based oncology care. 

. 2018;10:4591-4602. doi:10.2147/CMAR.S164201

15. Yang J, Chaudhry BI, Yue AT, et al. The impact of biosimilar use on total cost of care and provider financial performance in the Medicare Oncology Care Model: a population-based simulation study. 

. 2024;41(1):349-363. doi:10.1007/s12325-023-02703-x

16. Yang J, Carioto J, Pyenson B, et al. Greater uptake, an alternative reimbursement methodology needed to realize cost-saving potential of oncology biosimilars in the United States. 

. 2021;27(12):1642-1651. doi:10.18553/jmcp.2021.21202

17. Oncology Care Model. CMS. Accessed August 13, 2024. 

https://www.cms.gov/priorities/innovation/innovation-models/oncology-care

18. Chong A, Witherspoon E, Honig B, Ela E, Cavanagh H, Strawbridge L. Reflections on the Oncology Care Model and looking ahead to the Enhancing Oncology Model. 

. 2022;18(10):685-690. doi:10.1200/OP.22.00329

19. Mullangi S, Schleicher SM, Parikh RB. The Oncology Care Model at 5 years—value-based payment in the precision medicine era. 

. 2021;7(9):1283-1284. doi:10.1001/jamaoncol.2021.1512

20. EOM (Enhancing Oncology Model). CMS. Accessed August 13, 2024. 

https://www.cms.gov/priorities/innovation/innovation-models/enhancing-oncology-model

21. Kocher RP, Adashi EY. A new approach to cancer bundled payments in Medicare—the Enhancing Oncology Model. 

. 2023;4(1):e224904. doi:10.1001/jamahealthforum.2022.4904

The Enhancing Oncology Model (EOM): Request for Applications

. CMS; May 30, 2024. Accessed August 13, 2024. 

https://www.cms.gov/priorities/innovation/media/document/eom-rfa-2024

23. Limited data set (LDS) files: general information and policies. HHS guidance portal. September 15, 2020. Accessed August 13, 2024. 

https://web.archive.org/web/20240605014636/https://www.hhs.gov/guidance/document/limited-data-set-lds-files-general-information-and-policies

24. ASP pricing files. CMS. Accessed August 13, 2024. 

https://www.cms.gov/medicare/medicare-part-b-drug-average-sales-price/asp-pricing-files

OCM Performance-Based Payment Methodology: Version 7.1

. CMS; July 2021. Accessed August 13, 2024. 

https://www.cms.gov/priorities/innovation/files/x/ocm-pp3beyond-pymmeth.pdf

26. HCPCS quarterly update. CMS. Accessed August 13, 2024. 

https://www.cms.gov/medicare/coding/hcpcsreleasecodesets/hcpcs-quarterly-update

27. Mvasi. Prescribing information. Amgen Inc; 2023. Accessed August 13, 2024. 

https://www.accessdata.fda.gov/drugsatfda_docs/label/2023/761028s011lbl.pdf

28. Zirabev. Prescribing information. Pfizer Inc; 2023. Accessed August 13, 2024. 

https://www.accessdata.fda.gov/drugsatfda_docs/label/2023/761099s010lbl.pdf

29. Nyvepria. Prescribing information. Pfizer Inc; 2021. Accessed August 13, 2024. 

https://www.accessdata.fda.gov/drugsatfda_docs/label/2021/761111s004lbl.pdf

30. Ziextenzo. Prescribing information. Sandoz Inc; 2024. Accessed August 13, 2024. 

https://www.accessdata.fda.gov/drugsatfda_docs/label/2024/761045Orig1s014lbl.pdf

31. Udenyca. Prescribing information. Coherus BioSciences Inc; 2023. Accessed August 13, 2024. 

https://www.accessdata.fda.gov/drugsatfda_docs/label/2023/761039Orig1s015lbl.pdf

32. Fulphila. Prescribing information. Mylan Pharmaceuticals Inc; 2021. Accessed August 13, 2024. 

https://www.accessdata.fda.gov/drugsatfda_docs/label/2021/761075s012lbl.pdf

33. Truxima. Prescribing information. CELLTRION Inc; 2022. Accessed August 13, 2024. 

https://www.accessdata.fda.gov/drugsatfda_docs/label/2022/761088s018lbl.pdf

34. Riabni. Prescribing information. Amgen Inc; 2022. Accessed August 13, 2024. 

https://www.accessdata.fda.gov/drugsatfda_docs/label/2022/761140s001lbl.pdf

35. Ruxience. Prescribing information. Pfizer Ireland Pharmaceuticals; 2023. Accessed August 13, 2024. 

https://www.accessdata.fda.gov/drugsatfda_docs/label/2023/761103s011lbl.pdf

36. Nivestym. Prescribing information. Hospira Inc; 2024. Accessed August 13, 2024. 

https://www.accessdata.fda.gov/drugsatfda_docs/label/2024/761080s014lbl.pdf

37. Zarxio. Prescribing information. Sandoz Inc; 2024. Accessed August 13, 2024. 

https://www.accessdata.fda.gov/drugsatfda_docs/label/2024/125553s035lbl.pdf

38. Retacrit. Prescribing information. Hospira Inc; 2024. Accessed August 13, 2024. 

https://www.accessdata.fda.gov/drugsatfda_docs/label/2024/125545s032lbl.pdf

39. Kanjinti. Prescribing information. Amgen Inc; 2019. Accessed August 13, 2024. 

https://www.accessdata.fda.gov/drugsatfda_docs/label/2020/761073Orig1s001lbl.pdf

40. Ogivri. Prescribing information. Mylan GmbH; 2019. Accessed August 13, 2024. 

https://www.accessdata.fda.gov/drugsatfda_docs/label/2019/761074s004lbl.pdf

41. Ontruzant. Prescribing information. Samsung Bioepis Co Ltd; 2020. Accessed August 13, 2024. 

https://www.accessdata.fda.gov/drugsatfda_docs/label/2020/761100Orig1s005Lbl.pdf

42. Herzuma. Prescribing information. CELLTRION Inc; 2019. Accessed August 13, 2024. 

https://www.accessdata.fda.gov/drugsatfda_docs/label/2019/761091s001s002lbl.pdf

43. Trazimera. Prescribing information. Pfizer Ireland Pharmaceuticals; 2019. Accessed August 13, 2024. 

https://www.accessdata.fda.gov/drugsatfda_docs/label/2019/761081s000lbl.pdf

44. Chaudhry BI, Yue A, Kaila S, Sadik K, Tran L, Connor N. Impact of cancer- and patient-level factors on provider risk in the Oncology Care Model. 

 2021;39(suppl 15):e18852.
doi:10.1200/JCO.2021.39.15_suppl.e18852

45. Chaudhry BI, Gordon LN, Yue A, Tran L, Connor N. Evaluating differential cost growth across individual cancers: insights from Oncology Care Model data. 

 2021;39(suppl 15):e18853. doi:10.1200/JCO.2021.39.15_suppl.e18853

46. Limited data set (LDS) files: LDS general information. CMS. Updated April 25, 2024. Accessed August 13, 2024. 

https://web.archive.org/web/20240807102034/https://www.cms.gov/data-research/files-for-order/data-disclosures-and-data-use-agreements-duas/limited-data-set-lds

47. Harrison RL. Introduction to Monte Carlo simulation. 

Enhancing Oncology Model (EOM) Payment Methodology: Version 4.0

. Center for Medicare and Medicaid Innovation, CMS; June 5, 2024. Accessed August 13, 2024. 

https://web.archive.org/web/20241004125210/https://www.cms.gov/priorities/innovation/media/document/eom-payment-methodology

Evaluation of the Oncology Care Model: Final Report

. Center for Medicare and Medicaid Innovation, CMS; May 2024. Accessed August 13, 2024. 

https://www.cms.gov/priorities/innovation/data-and-reports/2024/ocm-final-eval-report-2024

50. Keating NL, Jhatakia S, Brooks GA, et al; Oncology Care Model Evaluation Team. Association of participation in the Oncology Care Model with Medicare payments, utilization, care delivery, and quality outcomes. 

. 2021;326(18):1829-1839. doi:10.1001/jama.2021.17642

Articles

Evidence suggests that adoption of oncology biosimilars in Medicare value-based payment models has produced substantial cost savings and improved provider financial performance.

Ahmed Shelbaya, MD

Biosimilar Adoption and Provider Performance in Medicare Value-Based Payment Models