Episode Resource Equivalents as a Novel Methodology to Accelerate Episode Payment Models in Total Joint Arthroplasty

The Centers for Medicare and Medicaid Services Innovation Center have deployed episode payment models in beneficiaries covered by traditional Medicare for over a decade. However, Medicare Advantage (MA) plans have been slow in implementing episodic models due to varying target prices within MA plans. This study validated a novel methodology overcoming this barrier by calculating an episode resource equivalent (ERQ) based on the occurrence of events quantifying clinical resource consumption, regardless of payor analogous to relative value units for physician clinical effort.

Claims data from a Medicare accountable care organization (ACO) were used to calculate the mean spend of 12 clinical events (ERQ events) that drive spending in the overall total joint arthroplasty care episode. Coefficients for the 12 ERQ events were calculated by dividing the mean Medicare spend of each ERQ event by the mean inpatient Medicare spend. The episode ERQ is the sum of all ERQ event coefficients incurred during the episode. The relationship between episode ERQs and total spending was validated via the ACO data warehouse (N = 3,316). Transferability of the ERQ methodology was then tested by using the ACO-based ERQ event coefficients in determining the relationship between episode ERQ and the total amount spent on episodes from a local MA plan (N = 700).

The mean episode ERQs for total joint arthroplasty surgeons were strongly and significantly associated with the actual episode spend for both ACO (P ≤ 0.001, R² = 0.8636, standardized coefficient = 0.0929, 95% confidence interval (CI), 0.808 to 1.051) and MA plans (P ≤ 0.001, R² = 0.887, standardized coefficient = 0.942, 95% CI, 0.688 to 1.195).

The ERQ is a novel methodology to measure clinical resource consumption that is transferable between payors for measuring performance targets in episode-based payment models. These data support ERQ as a payor-agnostic metric that can evaluate alternative payment model performance in value-based care.

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