Журнал клинических исследований и биоэтики

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A Data-driven Approach to Revamp the Accountable Care Organizations (ACO) Risk Adjustment Model

Yubin Park, Kevin Buchan, Jason Piccone, Brandon Sim

Accountable Care Organizations (ACOs) involve groups of healthcare providers, who voluntarily come together to deliver coordinated, high-quality care to aligned beneficiaries. Many ACOs, such as the Medicare shared savings program and the ACO REACH program, can participate in alternative payment models that differ from the prevalent Fee-for-Service model. In these alternative payment models, providers and payers share financial risk to align the ACOs’ financial incentives with the dual aims of reducing the total cost of care and improving the quality of care. In other words, ACOs could profit by keeping their patients healthy and preventing unnecessary hospitalization. However, to make this financial structure work as intended, there needs to be a Risk Adjustment (RA) model to change reimbursement proportional to a beneficiary’s risk; otherwise, ACOs may enroll only healthy patients, i.e., adverse selection. While most ACOs adopt RA models for this reason, the original RA methodology has mostly stayed the same over the last several decades. As a result, some ACO participants have found ways to “game” the system: to receive disproportional payments for the risk they bear. To mitigate the waste, the federal government has added various post-adjustment mechanisms, such as mixing the risk-adjusted benchmark with historical spending, adjusting by a coding intensity factor, capping risk score growth rate and incorporating health equity incentives. Unfortunately, those mechanisms build on top of each other in nonlinear and discontinuous ways, causing their actual effects and efficacy to be difficult to disentangle and evaluate. In this paper, we will summarize our lessons from operating one of the most successful ACOs in the nation to help rebuild the RA model based on a data-driven approach. Next, we outline the characteristics of an ideal RA model. Then, we propose a new one that addresses such requirements, eliminating the need for a multi-step process involving nonlinear and discontinuous staging. Finally, we provide experimental results by applying this model to our ACO data and comparing them with the current RA implementation. Our experimental results show that our data-driven approaches can achieve better predictive performances measured in R-squared, Cumming’s Prediction Measure, and Mean Absolute Prediction Error.