SummaryClinicians who treat certain types of cancer may be better positioned to earn performance-based payments under the Oncology Care Model.
New Avalere research finds that actual episode costs for certain cancers, including lung and liver cancers, may differ by as much as 8%, on average, from predicted episode costs under the Oncology Care Model (OCM). The findings were presented at the American Society of Clinical Oncology Quality Care Symposium and examine the costs of care and the implications for OCM performance-based payments for each of the 21 OCM cancer types.
The OCM is a voluntary 5-year bundled payment program with the goal of reducing costs and improving quality of cancer care. Currently, 179 oncology practices, representing more than 6,500 practitioners, and 13 payers in the US, participate in the program. According to previous Avalere research, more than 1 in 5 cancer patients in fee-for-service (FFS) Medicare receive care from physicians participating in OCM. The Medicare OCM provides physicians with performance-based payments in addition to traditional Medicare FFS reimbursements.
“As the shift towards value-based care continues, it is important to evaluate how new payment models like the Oncology Care Model may affect physicians and patients,” said Richard Kane, senior director at Avalere. “Our research suggests that clinicians who treat certain cancer types may perform better under the Oncology Care Model.”
Avalere’s research found that clinicians’ claims-based quality measure scores in the OCM also varied by cancer type. Under the OCM, a clinician’s performance-based payment is partly determined by how they perform on 12 specified quality measures, of which 3 are claims-based. The non-risk-adjusted average aggregate score for claims-based quality measures for bladder cancer episodes during the baseline period was 92%. Conversely, for acute leukemia and head-and-neck cancers, none of the possible quality points were achieved, on average. CMS indicates that claims-based quality measures will be risk-adjusted, but it has not made any details on its approach publicly available.
“Cancer episodes for which actual costs are greater than predicted costs and for which quality scores are low will adversely affect a participant’s ability to earn performance-based payments,” said Matt Brow, president of Avalere. “Identifying these types of challenges are essential to ensuring the Oncology Care Model succeeds in rewarding efficient and high-quality care.”
Funding for this research was provided by AstraZeneca. Avalere maintained full editorial control.
OCM episodes were constructed and analyzed using Medicare Part A/B FFS claims and Part D prescription drug event (PDE) data under a CMS research data use agreement. A cohort of patients, including all OCM-eligible Medicare FFS cancer patients receiving cancer treatment, that represented less than 20% of total Medicare beneficiaries was used. Episodes were not attributed to OCM participants–instead, episodes were constructed for all OCM-eligible Medicare FFS beneficiaries.
The OCM methodology developed by CMS was replicated for calculating actual and predicted episode costs to determine if ‘average cost performance’ (average actual episode costs relative to predicted costs) for some types of cancers would contribute positively or negatively toward a participant’s ability to earn performance-based payments. Average actual Medicare costs and ‘predicted costs’, as determined by the OCM Prediction Model, were compared for each of the 21 types of cancers included in OCM benchmarks during the baseline period from January 2012 through June 2015. OCM benchmarks are mostly the result of predicted costs, along with adjustments for the use of novel therapies.
OCM methodology was also replicated for calculating non-risk-adjusted aggregate quality scores for the claims-based quality measures to determine if ‘average quality performance’ for some types of cancers would contribute positively or negatively toward a participant’s ability to earn performance-based payments.
produces measurable results. Let's work together.