Related Experiment Video
Updated: Jan 30, 2026

Reconstitution of Actin-Based Motility with Commercially Available Proteins
Published on: October 28, 2022
Modeling Episode-Based Payments for Cancer Using Commercial Claims Data
Michael Polson1, Todd Lord1, Themmi Evangelatos1
11 Magellan Rx Management, Middletown, Rhode Island.
Background:
Innovative health care reimbursement models are gaining attention as a way to move away from a payment system that rewards quantity of service over quality of care. One such alternative payment model is episode-based payment, such as the Oncology Care Model (OCM) being piloted by the Center for Medicare & Medicaid Innovation.
Objective:
To adapt the OCM methodology to a commercially insured population to understand the challenges and potential implications of implementing an episode-based payment model in a commercial health plan.
Methods:
Administrative claims databases from 3 regional commercial health plans were used to identify continually eligible patients (aged ≥ 18 years) with breast cancer, lung cancer, melanoma, or chronic myelogenous leukemia (CML). Episode triggers were identified using the OCM methodology. In calculating the episode-based payments, adjustments to the OCM methodology were necessary to adapt the methodology to a commercial population, since not all Medicare data elements used in the OCM algorithm are available in commercial claims data.
Results:
The adapted OCM-like model was applied to data from 39,967 patients with 1 of 4 cancer types. Approximately 13% of patients had at least 1 episode per year and the average number of episodes per patient per year for patients with at least 1 episode ranged from 1.42 for patients with melanoma to 1.94 for patients with CML. The percentage of total annual costs included in episodes was 49%, 60%, 34%, and 52% for breast cancer, lung cancer, melanoma, and CML, respectively.
Conclusions:
As health care financing shifts to alternative payment models, insurers may look to adopt episode-based payments for oncology, similar to the OCM. This study shows that implementing an OCM-like model in a commercial health plan is feasible but will require adjustments to the OCM algorithm to make it implementable and applicable to populations beyond Medicare.
Disclosures:
This study was conducted by Magellan Rx Management with funding contributed by Novartis. Zacker is an employee of Novartis. The other authors are employed by Magellan Rx Management and have nothing to disclose.
More Related Videos
Related Concept Videos
Testing a Claim about Mean: Known Population SD
Estimating a population mean requires the samples to be distributed normally. The data should be collected from the randomly selected samples having no sampling bias. The sample size needed to be higher than 30, and most importantly, the population standard deviation should be already known.
In most realistic situations, the population standard deviation is often unknown, but in rare circumstances, when it...
Testing a Claim about Population Proportion
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
Testing a Claim about Standard Deviation
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
Testing a Claim about Mean: Unknown Population SD
Estimating a population mean requires the samples to be approximately normally distributed. The data should be collected from the randomly selected samples having no sampling bias. There is no specific requirement for sample size. But if the sample size is less than 30, and we don't know the population standard deviation, a different approach is used;...
Model Approaches for Pharmacokinetic Data: Physiological Models
Model Approaches for Pharmacokinetic Data: Compartment Models
Two primary types of compartment models are recognized: mammillary and catenary. The more...

