Health Care Resource Utilization, Quality Metrics, and Costs of Bladder Cancer Within the Oncology Care Model

Spending on novel therapies in high-risk bladder cancer had minimal impact on Oncology Care Model payments to practices, according to this cohort study and an average performance estimation.

Bladder cancer (BC) is the sixth most common cancer in the United States, with 83,730 new cases and 17,200 deaths in 2021.1,2 Although the 5-year survival rate for BC overall is 77.1%, it is only 6.4% for metastatic disease.2

Before 2016, the therapeutic management of BC had remained largely unchanged for more than 30 years, with treatment for non–muscle-invasive BC consisting of transurethral resection of bladder tumor with or without intravesical chemotherapy (eg, mitomycin) or immunotherapy (bacille Calmette-Guérin [BCG]), whereas treatment for muscle-invasive, advanced, or metastatic disease was dominated by radical cystectomy and systemic therapy or immunotherapy.3,4 The most prominent histological subtype of BC is urothelial cancer (UC), accounting for more than 90% of cases of BC.5 Historically, patients with locally advanced or metastatic UC (LA/mUC) have been treated with cisplatin-based chemotherapy. However, because BC primarily affects individuals 70 years and older, a considerable proportion of patients were ineligible for cisplatin-based chemotherapy because of characteristics such as poor performance status and age-related comorbidities.4 Patients with LA/mUC who are ineligible for cisplatin are treated with carboplatin-based regimens, but with limited effectiveness.4

Many new treatment options for BC have been approved by the FDA since 2016. With the introduction of immunotherapies targeting PD-1 or PD-L1, including pembrolizumab, avelumab, and nivolumab,6-9 the treatment landscape for BC, particularly LA/mUC, is considered to be especially dynamic and evolving.6-10 Since 2019, further novel therapies have been approved in the second- and third-line setting of LA/mUC, including enfortumab vedotin, sacituzumab govitecan, and erdafitinib.11-14

In 2016, the CMS Innovation Center introduced the Oncology Care Model (OCM), an alternative service delivery and payment model, to explore new mechanisms and payment and delivery structures, with the aim of delivering lower-cost and higher-quality oncology care to Medicare beneficiaries.15 The OCM was a 6-year model to test innovative payment strategies that promote high-quality and high-value care by oncology practices voluntarily participating in the model.16,17 The OCM was centered on 6-month episodes of care triggered by receipt of anticancer therapy. During each episode, practices were reimbursed on a fee-for-service (FFS) basis, which was derived from the mean expenditures determined for each cancer diagnosis group. The OCM also had a 2-part payment approach: the Monthly Enhanced Oncology Service (MEOS) payment and the potential performance-based payment (PBP). Monthly MEOS payments of $160 per beneficiary were paid to practices for enhanced services and care coordination, whereas potential PBPs incentivized practices to lower cost of care relative to a risk-adjusted target amount and to improve care quality.15,18 A benchmark episode expenditure at the end of each episode was determined based on historical data and was adjusted for novel therapy use (to partially compensate practices with higher-than-average novel therapy use) and patient-specific risk factors. A discount, depending on risk-sharing arrangement, was then applied to the benchmark episode expenditure to determine a target price. Practices could choose between a 1-sided risk-sharing arrangement (no additional payment from the practice to CMS when actual episode expenditures are higher than spending target) and a 2-sided arrangement (in which the practice is eligible for higher PBPs but is required to pay expenditures above spending target). Additional retrospective PBPs were made biannually, based on performance periods.16,19 The OCM was discontinued in December 2021, with payments ending in June 2022 for remaining episodes. It is anticipated to be replaced by a new episode-based alternative payment model, Oncology Care First (OCF). The OCF model will potentially include a shift from FFS reimbursement to capitation, improved PBP methodology, and an expansion of eligible beneficiaries.20

A recent review of alternative US payment and care delivery models in cancer care, such as the OCM, identified a lack of outcome reporting and therefore limited evidence to evaluate the efficiency and impact of these models.18 Given the dynamic BC treatment landscape and the potential impact of novel therapies on outcomes and costs, it is important to understand current practices and spending in BC management as a benchmark within the OCM alternative payment model.

The primary objective of this analysis was to estimate health care resource utilization (HCRU) and benchmark spending per OCM episode (overall, and high- and low-risk episodes) of BC, as well as spending drivers and quality metrics within practices modeled as though they are participating in the OCM. New and emerging therapies will affect spending and patient care in this payment model; we therefore aimed to contextualize the historical experience of BC among all OCM cancer types to lay a foundation and to gain an understanding of how the changing landscape may impact this benchmark. This foundational work will improve the understanding of managing patients with BC in OCM practices and alternative payment models and of how BC fits into the overall experience of an OCM practice.


Study Design

A retrospective cohort study was conducted among Medicare FFS beneficiaries with BC who qualified for an OCM 6-month episode of care based on the CMS OCM guidelines; these approximated episodes were triggered by receipt of cancer therapy. An average performance estimation was conducted using the results of the claims data analysis to assess how practices may perform differently based on their characteristics (eg, practice type, risk-sharing arrangement, utilization of medical and drug services, novel therapy uptake).

Study Population

The study population was derived from Medicare 100% Research Identifiable Files administrative claims data (with linked parts A [hospital insurance, including inpatient care, nursing facilities, hospice care, and home health care],21 B [medical insurance, including outpatient care, medical supplies, and preventive services],22 and D [prescription drug coverage]23) from January 1, 2016, through December 31, 2018. These were the most recent CMS claims data available at the time of study conduct. The episodes were constructed following CMS OCM methodologies: An episode starts with an identifying trigger event in the claims data that indicates provision of cancer therapy for any cancer diagnosis. A cancer type was assigned to each episode to identify BC episodes specifically. The period for the trigger events eligible for inclusion in the analysis was between July 1, 2016, and June 30, 2018, to ensure a 6-month baseline period and up to 6 months of postindex follow-up. BC episodes were stratified by risk based on OCM definition. Low-risk BC was defined by receipt of BCG and/or mitomycin without any other cancer therapy; these are therapies used for patients with non–muscle invasive BC only. High-risk BC was defined by receipt of cancer therapy other than BCG or mitomycin.

Inclusion criteria were aligned with CMS methodology for the OCM program. We required patients to (1) have a Part B or D cancer therapy trigger event (index date) with cancer diagnosis between July 1, 2016, and June 30, 2018; (2) have been enrolled in both parts A and B for the entire OCM episode (6 months or until death); (3) have Medicare as their primary payer; (4) have at least 1 qualifying evaluation and management visit during the episode; and (5) have 6 months’ enrollment preindex and post index (or death).

CMS OCM methodology did not require a 6-month baseline period when considering episodes. New episodes could not be triggered while a 6-month episode was already ongoing. Patients covered under Medicare Advantage or any other group health program, or patients receiving Medicare benefits for end-stage renal disease, were excluded.

Among patients with a potential OCM triggering event, those with a diagnosis code for metastatic BC (International Classification of Diseases, Tenth Revision, Clinical Modification [ICD-10-CM] codes C77.XX, C78.XX, C79.XX) were described in an exploratory analysis. In its framework, CMS OCM does not specifically consider metastatic BC—only high- and low-risk BC.


The outcomes of interest are the following:

  1. HCRU (including hospitalizations and intensive care unit [ICU] stays, lengths of stay, emergency department [ED] visits, cancer therapy use, and adverse events [AEs]).
  2. OCM quality metrics (including metrics affecting payments for performance periods 1 through 4 in the CMS OCM [2016-2018]; proportion of episodes with all-cause hospital admissions within the 6-month episode; proportion of episodes with all-cause ED visits or observation stays that did not result in a hospital admission within the 6-month episode; proportion of episodes with hospice for 3 days or more, among patients who died within the 6-month episode; and proportion of patients who died during the episode).
  3. Costs (including total cost per episode, inpatient services, outpatient services, cancer therapies, physician services, other Part B drugs, other Part D drugs, hospice care, and other services [eg, home health or skilled nursing facility]).

Statistical Analysis

Descriptive data summaries are reported, such as means, CIs, frequencies, and percentages.

Average Performance Estimation

An average performance estimation was conducted based on input values for hypothetical OCM practices to assess how practices performed differently from one another. The model was based on practice characteristics (eg, practice type, risk-sharing arrangement, utilization of medical and drug services, and uptake of parts B and D novel therapies); proportion of patients with high- or low-risk BC; and the change in HCRU or Part B novel cancer therapy costs among patients with high-risk BC. Input values for the average performance estimation were derived from the retrospective cohort study and included practice-specific inputs, BC inputs, and OCM high-risk BC inputs. The default setting was an average integrated delivery network with a 1-sided risk-sharing arrangement (eAppendix Table 1 [eAppendix available at]).

Separate analyses testing different scenarios were conducted to assess the impact of no increase vs a 25% increase in spending on novel therapy in high-risk BC under a 1-sided and a 2-sided risk arrangement. The 1-sided risk arrangement assumed no reduction in utilization of medical and drug services, whereas in the 2-sided scenario, a 10% reduction in utilization of medical and drug services was assumed.


Study Population

Of the approximately 2.2 million OCM episodes identified for all cancer types among approximately 1 million Medicare beneficiaries, 60,099 (2.7%) were BC episodes. Our study cohort consisted of 43,621 BC episodes among 33,497 beneficiaries with BC, with 31.0% of the BC episodes identified as high risk (eAppendix Figure 1). Relative to patients who experienced low-risk episodes, high-risk episodes included a higher proportion of metastatic cases (40% vs 2%), based on post index episodes.

Across BC episodes, patients were predominantly White (92%) and male (77%), and they had a mean age of 76.6 years (Table 1). Relative to patients who experienced low-risk episodes, those with high-risk BC episodes had a higher comorbidity burden at baseline (mean Charlson Comorbidity Index score, 7.4 vs 4.3; 68% vs 35% of patients had 5 or more comorbidities). Hypertension, dyslipidemia, and coronary artery disease/atherosclerosis were the most commonly reported comorbid conditions across BC episodes.

HCRU and Quality Metrics per 6-Month OCM BC Episodes

Low-risk episodes of BC had a mean of 0.2 hospital admissions per episode, a mean length of stay of 4.9 days, 1% BC surgery rate, 2% mortality rate, and 5% of hospitalizations with ICU stays; 24% of episodes required ED visits. Among OCM quality metrics, low-risk episodes were associated with 15% of inpatient admissions and 20% of ED visits without inpatient admissions. The most common AEs reported during low-risk episodes were anemia and fatigue (Table 2).

High-risk episodes of BC had a mean of 0.7 hospital admissions per episode, a mean length of stay of 5.9 days, 7% BC surgery rate, 17% mortality rate, and 17% of hospitalizations with ICU stays; 47% of episodes required ED visits. Among OCM quality metrics, high-risk episodes were associated with 42% of inpatient admissions and 37% of ED visits without inpatient admissions. The most common AEs reported during high-risk episodes were also anemia and fatigue (Table 2).

Costs per Episode

Mean (SD) spending per high-risk BC episode was $37,857 ($30,433), with $11,051 spent on cancer therapies and $7158 on inpatient services. For low-risk BC episodes, the mean (SD) spending per episode was $9204 ($13,508), with $683 spent on cancer therapies and $2081 on inpatient services (Figure).

Episodes with metastatic BC had higher total costs ($47,478), as well as higher component costs, with $14,141 per episode spent on cancer therapies and $10,531 on inpatient services. However, based on CMS classification, metastatic BC episodes are grouped as high risk along with other, nonmetastatic, or locally advanced episodes.

Average Performance Estimation

The average performance estimation resulted in 2116 total OCM episodes for all cancers in a 6-month period, with 28 (1.3%) high-risk BC episodes and 31 (1.5%) low-risk BC episodes (Table 3). The mean spend was $32,043 per OCM episode across cancers, with a total 6-month episode spending of $67.8 million. The mean spend was $35,310 per high-risk and $9263 per low-risk BC episode, which marginally exceeds OCM performance period spending targets of $34,712 (high-risk BC) and $8391 (low-risk BC). Although high-risk BC exceeded the 6-month spending target by 1.7% and low-risk BC by 9.4% in the average performance estimation, this does not affect the PBP because the performance estimation assumed a 1-sided risk-sharing arrangement with zero downside risk for OCM recoupment. In this scenario, the retrospective PBP from CMS was $0. Trends in spending per episode by service type were similar to those observed in the retrospective analysis(eAppendix Figure 2).

Scenario Analysis

In the separate scenario analysis comparing a 0% vs 25% increase in novel therapy drug spending for OCM high-risk BC episodes under a 1-sided risk arrangement, a 25% increase in spending resulted in no change of the PBP (eAppendix Table 2).

In the scenario analysis under a 2-sided risk arrangement, for a 0% vs 25% increase in novel therapy drug spending, there was an overall incremental gain for practices on all OCM expenditures of only 0.1%, or $23, per OCM episode (eAppendix Table 3).


In our analysis, BC accounted for approximately 3% of all OCM 6-month episodes, and one-third of OCM BC episodes among Medicare beneficiaries were classified as high risk. High-risk OCM episodes of BC—which involved individuals receiving systemic therapies, 40% of whom had metastatic BC—had higher HCRU and costs, and lower-quality performance, than low-risk episodes.

High-risk BC episodes were characterized by more advanced disease, exhibited by the high proportion of patients with metastatic disease, which indicates that high-risk episodes are primarily assigned to patients with muscle-invasive BC and LA/mUC. Novel therapies offer a significant opportunity to optimize BC management and improve quality of care, particularly for high-risk episodes, where we demonstrated that costs of inpatient services were highest.24

The cost impact to the OCM for introducing or increasing novel therapy use in BC episodes is low. Because low-risk episodes can include only BCG and mitomycin therapy, all novel anti-BC treatments will trigger high-risk episodes. Controlling expenditures on novel therapies in BC episodes is unlikely to affect overall episode spending for participating practices, because the novel therapy adjustment is calculated at the overall practice level for all cancers, not by cancer type. The low volume of BC episodes (~3%) across all cancers also makes the managing of high-risk BC, which represents a subset of this already small patient population, unlikely to affect overall performance. Similarly, although low-risk BC makes up the majority of the BC episodes, the mean cost of a low-risk BC episode is only one-third of the mean OCM episode. Together with the overall low volume of BC episodes among all cancer episodes, this means that low-risk BC is also unlikely to be a suitable target for cost containment.

In addition to the retrospective analysis, an average performance estimation was conducted to explore potential differences in OCM practice characteristics. In the average performance estimation, a 1-sided risk arrangement was assumed in the default scenario, which does not result in an additional payment from the practice to CMS when actual expenditures are above spending target. In a 2-sided risk arrangement, the practice is eligible for higher PBPs, and recoupment payments to CMS are due when expenditures are above target. The exceeded novel therapy drug spending targets observed in the 1-sided risk arrangement do not affect the PBP to practices; the 2-sided risk arrangement showed that exceeding novel therapy drug spending targets by 25% had a negligible effect, with an impact of only 0.1% on overall costs. This emphasizes the minimal impact that controlling novel therapy spending in high-risk BC has on OCM PBPs.

The costs observed in this retrospective analysis and average performance estimation for high-risk episodes of BC were consistent with costs reported in a retrospective analysis of patients diagnosed with LA/mUC who were identified using the 100% sample of the Medicare FFS Research Identifiable Files.25 In that analysis, patients incurred mean costs of $7153 per member per month (ie, an estimated $42,918 over 6 months) among LA/mUC treated with PD-1/PD-L1 inhibitors.25 Given the highly dynamic treatment landscape for BC, particularly the evolvement of immunotherapies, this further confirms a clinically relevant economic benchmark for treatment costs for metastatic BC.


This study has some limitations. The most recent available administrative claims data were for 2016 to 2018 at the time of analysis, before the introduction of the latest novel therapies for BC since 2019 (including enfortumab vedotin, sacituzumab govitecan, and erdafitinib11-14), which further highlights the dynamic treatment landscape of BC. Outcome metrics and study conclusions do not reflect other changes that occurred after the study period (eg, benefit coverage changes, additional approved drugs, changes to standard of care). However, the average performance estimation explored changes beyond the study period, and even an increase in novel therapy spending by as much as 25% did not affect the overall conclusion. Although a 25% increase was selected as the likely upper limit of increased spending on novel therapies, other percentage increases (such as 30%) would have a similarly negligible effect on overall costs. Furthermore, Medicare FFS claims data have inherent limitations (eg, cancer-specific clinical factors such as anatomical cancer stage, histology, biomarkers, or molecular mutations are not captured and cannot be adjusted for). Additionally, classification of metastatic episodes was based on ICD-10-CM codes, representing potential for misclassification and/or undercoding.


High-risk OCM episodes of BC, 40% of which included patients with metastatic BC, had higher HCRU and costs and lower-quality performance than low-risk episodes. Novel therapies offer an opportunity to optimize BC management and improve quality of care, particularly for high-risk episodes.24,26,27 Given that less than 3% of all OCM cancer episodes were attributed to BC, and that only one-third of these were classified as high risk, controlling expenditures on novel therapies in BC episodes is unlikely to affect overall performance for practices participating in the OCM.

The OCM is an example of an alternative payment model in oncology and can serve to inform other initiatives in this space. The OCF model would be a new and distinct model, building on lessons learned. Further studies should evaluate long-term OCM practice performance, accounting for the impact of emerging therapies on outcomes and costs over time, and following implementation of the forthcoming OCF model.


Medical writing support was provided by Vanessa Gross, of Curo (a division of Envision Pharma Group), and funded by Seagen Inc.

Author Affiliations: Seagen Inc (HSW, ZH, SG, PW), Bothell, WA; Astellas Pharma Inc (RF), Northbrook, IL; Milliman Inc (ST, JH, GD), New York, NY; Tennessee Oncology (LJB), Hermitage, TN.

Source of Funding: Seagen Inc and Astellas Pharma Inc.

Author Disclosures: Dr Wirtz is an employee of Seagen Inc, which provided funding for this manuscript, and owns stock in Seagen, Amgen Inc, and Teva Pharmaceutical Industries Ltd. Mr Hepp, Dr Grewal, and Dr Wright are employees of Seagen and hold stock/stock options in Seagen. Ms Fuldeore is an employee of Astellas Inc, which provided funding for this manuscript. Ms Tomicki, Mr Hirsch, and Ms Dieguez are employees of Milliman Inc, which received funding from Seagen and Astellas Pharma Inc in connection with this research. Dr Blakely received funding from Seagen and Astellas Pharma Inc in connection with this research.

Authorship Information: Concept and design (HSW, ZH, SG, PW, RF, ST, JH, GD, LJB); acquisition of data (ZH, SG, PW, RF, ST, JH, GD, LJB); analysis and interpretation of data (HSW, ZH, SG, PW, RF, ST, JH, GD, LJB); drafting of the manuscript (HSW, ZH, SG, PW, RF, ST, JH, GD, LJB); critical revision of the manuscript for important intellectual content (HSW, ZH, SG, PW, RF, ST, JH, GD, LJB); statistical analysis (ST, JH, GD); provision of patients or study materials (HSW, ZH, SG, PW, RF, ST, JH, GD, LJB); obtaining funding (HSW, ZH, SG, PW, RF); administrative, technical, or logistic support (HSW, ZH, SG, PW, RF, ST, JH, GD, LJB); and supervision (HSW, ZH, SG, PW, RF, ST, JH, GD, LJB).

Address Correspondence to: Heidi S. Wirtz, PharmD, PhD, Seagen Inc, 21717 30th Dr SE, Bothell, WA 98021. Email:


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