Metric Selection, Metric Targets, and Risk Adjustment Should be Considered in the Design of Gainsharing Models for Bundled Payment Programs in Total Joint Arthroplasty

• Published in: The Journal of arthroplasty Vol. 36; no. 3; pp. 801 - 809
• Main Authors: Keswani, Aakash H., Snyder, Daniel J., Ahn, Amy, Austin, Daniel C., Jayakumar, Prakash, Grauer, Jonathan N., Poeran, Jashvant, Bozic, Kevin J., Moschetti, Wayne E., Jevsevar, David S., Galatz, Leesa M., Bronson, Michael J., Chen, Darwin D., Moucha, Calin S.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.03.2021
• Subjects: alternative payment models; arthroplasty; Arthroplasty, Replacement, Hip; Arthroplasty, Replacement, Knee; bundled payments; gainsharing; Humans; Patient Care Bundles; Patient Discharge; Risk Adjustment; United States; United States; risk adjustment; bundled payments; gainsharing; arthroplasty; alternative payment models
• ISSN: 0883-5403; 1532-8406
• DOI: 10.1016/j.arth.2020.10.007

Under bundled payment models, gainsharing presents an important mechanism to ensure engagement and reward innovation. We hypothesized that metric selection, metric targets, and risk adjustment would impact surgeons’ performance in gainsharing models. Patients undergoing total joint arthroplasty at an urban health system from 2017 to September 2018 were included. Gainsharing metrics included the following: length of stay, % discharge-to-home, 90-day readmission rate, % of patients with episode spend under target price, and % of patients with patient-reported outcomes (PROs) collected. Four scenarios were created to evaluate how metric selection/adjustment impacted surgeons’ performance designation: scenario 1 used “aspirational targets” (>60th percentile), scenario 2 used “acceptable targets” (>50th percentile), scenario 3 risk-adjusted surgeon performance prior to comparing aspirational targets, and scenario 4 included a PRO collection metric. Number of metrics achieved determined performance tier, with higher tiers getting a greater share of the gainsharing pool. In total, 2776 patients treated by 12 surgeons met inclusion criteria (mean length of stay 3.0 days, readmission rate 4.0%, discharge-to-home 74%, episode spend under target price 85%, PRO collection 56%). Lowering of metric targets (scenario 1 vs. 2) resulted in a 75% increase in the number of high performers and 98% of the gainsharing pool being eligible for distribution. Risk adjustment (scenario 3) caused 50% of providers to move to higher performance tiers and potential payments to increase by 28%. Adding the PRO metric did not change performance. Quality metric/target selection and risk adjustment profoundly impact surgeons’ performance in gainsharing contracts. This impacts how successful these contracts can be in driving innovation and dis-incentivizing the “cherry picking” of patients. Level III.