Medicare Spending, Utilization, and Quality in the Oncology Care Model

JAMA ◽  
2021 ◽  
Vol 326 (18) ◽  
pp. 1805
Author(s):  
Raymond U. Osarogiagbon ◽  
Samyukta Mullangi ◽  
Deborah Schrag
Author(s):  
Mary Beth Arensberg ◽  
Beth Besecker ◽  
Laura Weldishofer ◽  
Susan Drawert

AbstractThe Oncology Care Model (OCM) is a US Centers for Medicare & Medicaid Services (CMS) specialty model implemented in 2016, to provide higher quality, more highly coordinated oncology care at the same or lower costs. Under the OCM, oncology clinics enter into payment arrangements that include financial and performance accountability for patients receiving chemotherapy treatment. In addition, OCM clinics commit to providing enhanced services to Medicare beneficiaries, including care coordination, navigation, and following national treatment guidelines. Nutrition is a component of best-practice cancer care, yet it may not be addressed by OCM providers even though up to 80% of patients with cancer develop malnutrition and poor nutrition has a profound impact on cancer treatment and survivorship. Only about half of US ambulatory oncology settings screen for malnutrition, registered dietitian nutritionists (RDNs) are not routinely employed by oncology clinics, and the medical nutrition therapy they provide is often not reimbursed. Thus, adequate nutrition care in US oncology clinics remains a gap area. Some oncology clinics are addressing this gap through implementation of nutrition-focused quality improvement programs (QIPs) but many are not. What is needed is a change of perspective. This paper outlines how and why quality nutrition care is integral to the OCM and can benefit patient health and provider outcomes.


2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e18852-e18852
Author(s):  
Basit Iqbal Chaudhry ◽  
Andrew Yue ◽  
Shuchita Kaila ◽  
Kay Sadik ◽  
Lisa Tran ◽  
...  

e18852 Background: Transferring financial risk from payers to providers to align incentives is central to value-based payment (VBP) reform, including Medicare’s Oncology Care Model (OCM). We simulated the impact of selected cancer- and patient-level factors on providers’ risk in OCM for multiple myeloma (MM), due to its clinical complexity. We hypothesize that risk exposure is sensitive to factors extrinsic to the OCM methodology, including clinical phenotype, disease state and progression rate. Methods: Simulation was used to address omitted variable bias in payer data. We developed 9 key clinical MM scenarios to examine provider risk, based on conceptual frameworks that included patient- and cancer-level factors. The model was parameterized using the Medicare limited data set, research literature and domain knowledge. Twenty factors were varied for each model, e.g. age, autologous stem cell transplant (ASCT). Results: Simulations results showed MM risk for providers depended highly on cancer and patient level factors (see table). For example, high-risk patients were on average $21.5K over target while undergoing ASCT (despite risk adjustment for ASCT) and $18-28K under target for follow on maintenance (maint.) episodes. Conclusions: Provider exposure to risk in OCM is highly sensitive to factors at the cancer and patient level. The distribution of clinical phenotypes, state of disease, and rate of disease progression can significantly impact risk exposure for providers in OCM. New methodologies that model risk in more clinically granular ways are needed to improve VBP in oncology. [Table: see text]


2021 ◽  
pp. OP.21.00050
Author(s):  
Joel E. Segel ◽  
Eric W. Schaefer ◽  
Nicholas G. Zaorsky ◽  
Christopher S. Hollenbeak ◽  
Haleh Ramian ◽  
...  

PURPOSE: With the introduction of the Oncology Care Model and plans for the transition to Oncology Care First, alternative payment models (APMs) are an increasingly important piece of the oncology care landscape. Evidence is mixed on the Oncology Care Model's impact on utilization and costs, but as policymakers consider expansion of similar models, it is critical to understand the characteristics of hospitals that may be differentially affected. METHODS: We used 2007-2016 SEER-Medicare data to identify patients with breast and prostate cancer receiving chemotherapy, endocrine therapy (breast), or androgen deprivation therapy (prostate). For each hospital, we calculated 6-month expected mortality, emergency department (ED) visits, inpatient admissions, and costs, all commonly collected APM outcomes. After calculating observed-to-expected rates for each outcome by hospital, we estimated the association between observed-to-expected rates and characteristics of each hospital to understand hospital characteristics that might be associated with higher- or lower-than-expected rates of each outcome. RESULTS: Hospitals with > 15% rural patients had significantly higher-than-expected mortality (0.31 points higher, P < .001) and ED visit rates (0.10 points higher, P = .029) as well as significantly lower costs (0.06 points lower, P = .004). Hospitals unaffiliated with a medical school also experienced significantly higher-than-expected mortality and ED visits. Hospitals eligible for disproportionate share hospital payment experienced significantly higher ED visits but lower costs. For-profit hospitals experienced higher-than-expected mortality. CONCLUSION: Rural hospitals and those unaffiliated with a medical school may require special consideration as APMs expand in oncology care. Designated cancer centers and larger hospitals may be advantaged.


2017 ◽  
Vol 101 (5) ◽  
pp. 569-571 ◽  
Author(s):  
LK Mortimer ◽  
LM Strawbridge ◽  
EW Lukens ◽  
A Bassano ◽  
PH Conway ◽  
...  
Keyword(s):  

2021 ◽  
Author(s):  
Ryan B. Thomas ◽  
Vittorio Maio ◽  
Anna Chen ◽  
Seojin Park ◽  
Dexter Waters ◽  
...  

PURPOSE: To explore mean difference between Oncology Care Model (OCM) total costs and target price among breast cancer episodes by stage under the Centers for Medicare and Medicaid Services OCM payment methodology. METHODS: Breast cancer episodes from OCM performance period 1-4 reconciliation reports (July 1, 2016-July 1, 2018) were linked with health record data from a large, academic medical center. Demographics, total cost of care (TCOC), and target price were measured by stage. Adjusted differences between TCOC and target price were compared across cancer stage using multivariable linear regression. RESULTS: A total of 539 episodes were evaluated from 252 unique patients with breast cancer, of which 235 (44%) were stage I, 124 (23%) stage II, 33 (6%) stage III, and 147 (27%) stage IV. About 37% of episodes exceeded target price. Mean differences from target price were –$1,782, $2,246, –$6,032, and $11,379 all in US dollars (USD) for stages I through IV, respectively. Stage IV episodes had highest mean TCOC ($44,210 USD) and mean target price ($32,831 USD) but also had higher rates of chemotherapy, inpatient admission, and novel therapy use. After adjusting for covariates, stage IV and ≥ 65-year-old patients had the highest mean difference from target price ($17,175 USD; 95% CI, $12,452 to $21,898 USD). CONCLUSION: Breast cancer episodes in older women with distant metastases most frequently exceeded target price, suggesting that target price did not adequately account for complexity of metastatic cancers. A metastatic adjustment introduced in PP7 represents a promising advancement in the target price methodology and an impact evaluation will be needed.


JAMA ◽  
2021 ◽  
Vol 326 (18) ◽  
pp. 1829
Author(s):  
Nancy L. Keating ◽  
Shalini Jhatakia ◽  
Gabriel A. Brooks ◽  
Amanda S. Tripp ◽  
Inna Cintina ◽  
...  

2020 ◽  
Vol 1 (1) ◽  
pp. 92-96 ◽  
Author(s):  
J. Russell Hoverman ◽  
B. Brooke Mann ◽  
Sara Phu ◽  
Philip Nelson ◽  
Jad E. Hayes ◽  
...  
Keyword(s):  

2019 ◽  
Vol 15 (10) ◽  
pp. e897-e905 ◽  
Author(s):  
Ravi B. Parikh ◽  
Justin E. Bekelman ◽  
Qian Huang ◽  
Joseph R. Martinez ◽  
Ezekiel J. Emanuel ◽  
...  

PURPOSE: The Oncology Care Model (OCM) is Medicare’s first bundled payment program for patients with cancer. We examined baseline characteristics of OCM physician participants and markets with high OCM physician participation to inform generalizability and complement the ongoing practice-level evaluation of the OCM. METHODS: In this cross-sectional study, we identified characteristics of US medical oncologists practicing in 2016, using a national telephone-verified physician database. We linked these data with Dartmouth Atlas and Medicare claims data from 2011 through 2016 to identify characteristics of markets with high OCM participation. We used logistic regression to examine relationships between market characteristics and OCM participation. RESULTS: Of 10,428 US medical oncologists, 2,605 (24.9%) were listed in an OCM practice. There were no differences in sex or medical training between OCM participants and nonparticipants, although OCM participants were slightly younger. OCM participants practiced in larger (median daily patient volume, 80 v 55 patients) and urban practices (95.2% v 90.7%) and were less likely to be part of a health system (41.0% v 60.4%) or solo practice (45.5% v 67.4%; all P < .001). Participation was higher in southern and mid-Atlantic markets. Markets with high OCM physician participation had higher specialist density, hospital care intensity, and acute care use at the end of life (all P < .001). Market-level penetration of Accountable Care Organizations (adjusted odds ratio, 4.65; 95% CI 3.31 to 6.56; P < .001) and Medicare Advantage (adjusted odds ratio 2.82; 95% CI, 1.97 to 4.06; P < .001) were associated with higher OCM participation. CONCLUSION: In the first description of oncologists participating in the OCM, we found differences in practice demographics, care intensity, and exposure to nontraditional payment models between OCM-participating and nonparticipating physicians. Such provider-level differences may not be captured in Medicare’s practice-level analysis.


2016 ◽  
Vol 36 ◽  
pp. e109-e114 ◽  
Author(s):  
Christian A. Thomas ◽  
Jeffrey C. Ward
Keyword(s):  

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