The impact of oral oncology drugs in the era of value-based care.

15 Background: Oncology drug expenditures are a significant contributor to increasing total cost of care (TCOC). Oral oncolytics (OO), such as small molecule inhibitors (CDK 4/6 inhibitors, tyrosine kinase inhibitors, etc.), and immunomodulators, have revolutionized the treatment landscape for many cancers. We evaluated the patterns of OO use and spend for Medicare patients over time at a large community oncology practice participating in the Center for Medicare and Medicaid Innovation (CMMI)’s Value-based care (VBC) Program, the Oncology Care Model (OCM), with a focus on the seven cancers that are part of a successor model, the Enhancing Oncology Model. Methods: Using CMMI claims for attributed 6-month OCM episodes, we evaluated drug use and expenditures by performance period (PP) from July 2016 – June 2022 (11 PPs), with an emphasis on OO drugs. We used Part D expenditures as a proxy for OO. Results: From 2016-22, average TCOC per PP increased by 15.5% in the 7 EOM diagnoses (breast, lung, colon, prostate cancer, myeloma, lymphoma, and chronic leukemia). Average drug expenditures per PP grew 33% percent and represented 71% of total cost by PP11, up from 61% in PP1. Average expenditures per episode increased by 11.7% for Part B and 82.6% for Part D drugs (p<0.01). Increase in Part D drug expenditures was partly due to an increase in the number of unique oral drugs which grew by 32% over the evaluation period. The greatest increases in Part D drug expenditures occurred in breast cancer (87%) and lymphoma (153.7%). Table reports average OO expenditures per PP, percent change from PP1 to PP11, and the change in the % Part D drugs contributed to TCOC. Conclusions: Increases in drug expenditures represent the greatest impact on TCOC for cancer patients. OO medicines have an increasing role in the treatment landscape and represent a growing focus in VBC models. Utilization and cost of OO medication have a significant bearing on VBC performance. Diagnosis Avg. OO Expenditures PP1 Avg. OO Expenditures PP11 Avg. OO Expenditures Change Contribution of OO to TCOC Change from PP1 - 11 Breast $4,208 $7,869 87.0% 9.9% Leukemia $25,334 $41,128 62.3% 19.9% CRC $1,206 $2,158 78.9% 3.1% Lung $3,650 $3,625 -0.7% -2.5% Lymphoma $4,193 $10,637 153.7% 13.0% Myeloma $25,109 $39,298 56.5% 6.7% Prostate $18,815 $20,114 6.9% 3.2% Total $8,291 $15,140 82.6% 10.8%

Authors

Institutions

Publication Details

Journal
JCO Oncology Practice
Published
2024-09-30
DOI
https://doi.org/10.1200/op.2024.20.10_suppl.15
Primary Topic
Economic and Financial Impacts of Cancer
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The impact of oral oncology drugs in the era of value-based care.

Puneeth Indurlal, Neal Dave, Jody S. Garey, Gury K. Doshi et al.
JCO Oncology Practice
Economic and Financial Impacts of Cancer
article

The impact of oral oncology drugs in the era of value-based care.

Puneeth Indurlal, Neal Dave, Jody S. Garey, Gury K. Doshi, Susan Marie Escudier, Hope Ives Thomas
article en

Abstract

15 Background: Oncology drug expenditures are a significant contributor to increasing total cost of care (TCOC). Oral oncolytics (OO), such as small molecule inhibitors (CDK 4/6 inhibitors, tyrosine kinase inhibitors, etc.), and immunomodulators, have revolutionized the treatment landscape for many cancers. We evaluated the patterns of OO use and spend for Medicare patients over time at a large community oncology practice participating in the Center for Medicare and Medicaid Innovation (CMMI)’s Value-based care (VBC) Program, the Oncology Care Model (OCM), with a focus on the seven cancers that are part of a successor model, the Enhancing Oncology Model. Methods: Using CMMI claims for attributed 6-month OCM episodes, we evaluated drug use and expenditures by performance period (PP) from July 2016 – June 2022 (11 PPs), with an emphasis on OO drugs. We used Part D expenditures as a proxy for OO. Results: From 2016-22, average TCOC per PP increased by 15.5% in the 7 EOM diagnoses (breast, lung, colon, prostate cancer, myeloma, lymphoma, and chronic leukemia). Average drug expenditures per PP grew 33% percent and represented 71% of total cost by PP11, up from 61% in PP1. Average expenditures per episode increased by 11.7% for Part B and 82.6% for Part D drugs (p<0.01). Increase in Part D drug expenditures was partly due to an increase in the number of unique oral drugs which grew by 32% over the evaluation period. The greatest increases in Part D drug expenditures occurred in breast cancer (87%) and lymphoma (153.7%). Table reports average OO expenditures per PP, percent change from PP1 to PP11, and the change in the % Part D drugs contributed to TCOC. Conclusions: Increases in drug expenditures represent the greatest impact on TCOC for cancer patients. OO medicines have an increasing role in the treatment landscape and represent a growing focus in VBC models. Utilization and cost of OO medication have a significant bearing on VBC performance. Diagnosis Avg. OO Expenditures PP1 Avg. OO Expenditures PP11 Avg. OO Expenditures Change Contribution of OO to TCOC Change from PP1 - 11 Breast $4,208 $7,869 87.0% 9.9% Leukemia $25,334 $41,128 62.3% 19.9% CRC $1,206 $2,158 78.9% 3.1% Lung $3,650 $3,625 -0.7% -2.5% Lymphoma $4,193 $10,637 153.7% 13.0% Myeloma $25,109 $39,298 56.5% 6.7% Prostate $18,815 $20,114 6.9% 3.2% Total $8,291 $15,140 82.6% 10.8%

JCO Oncology PracticeVol. 20(10_suppl)
Texas Oncology (US), The US Oncology Network (US)
Openalex Percentile: Top 79%
Economic and Financial Impacts of Cancer
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.