Navigating financial risk: Unlocking the power of modeling and risk quantification in episode-based payment models.

28 Background: The Centers for Medicare and Medicaid Innovation’s (CMMI) Enhancing Oncology Model (EOM) is a voluntary value-based care (VBC) model that requires physician group practices (PGPs) to assume financial risk and performance accountability for episodes of care related to seven common cancers. However, PGPs often lack the means to assess the financial impact and risks associated with these alternative payment models. To address this, we employed Monte Carlo Simulations (MCS) to calculate the likelihood of outcomes and associated risks. This information can assist PGPs in making informed decisions about assuming risk and participating in the EOM. Methods: Using historical episode files from 2019 to 2022 for 23 PGPs in The US Oncology Network, along with the EOM Price Prediction Model provided by CMMI, we calculated the predicted baseline price and savings per episode. We then determined the average savings for each PGP per time-period and utilized the MCS technique to simulate savings and risk, under each EOM risk arrangement. We shared the simulation results with the PGPs to support their decision-making process and participation. Results: Despite having enough episodes per time-period (ranging from 105 to 6100) for the 23 PGPs, the limited number of time-based populations (only 6) based on case mix and patient volume made traditional probability assessments inadequate. To overcome this, we utilized MCS with 5000 simulations per PGP, assuming a normal distribution based on population characteristics. By assessing the savings outcomes of each simulation, we developed probability distributions and confidence intervals to determine the likelihood of generating shared savings, recoupment, or landing in the neutral zone within the EOM. Factors such as episode counts, baseline price, and risk arrangement had the greatest impact on the range of outcomes. We shared the MCS results with all 23 PGPs, highlighting the limitations of the original data and facilitating their decision-making process. Ultimately, 12 PGPs chose to assume risk based on the probability of financial outcomes with the model, and to select the appropriate risk arrangement in the EOM. Conclusions: Compared to the status quo measures of central dispersion, MCS yields a wide range of expected financial outcomes per PGP. MCS is a robust technique for quantifying outcome probabilities in VBC models despite low data volumes but relies on the representativeness and quality of the original data. Conducting sensitivity analysis with MCS to assess the impact of individual assumptions or data inputs on outcome probabilities is an area for further exploration.