Improving target price calculations in Medicare bundled payment programs.

ABSTRACT

OBJECTIVE: To compare the predictive accuracy of two approaches to target price calculations under Bundled Payments for Care Improvement-Advanced (BPCI-A) the traditional Centers for Medicare and Medicaid Services (CMS) methodology and an empirical Bayes approach designed to mitigate the effects of regression to the mean. DATA SOURCES Medicare fee-for-service claims for beneficiaries discharged from acute care hospitals between 2010 and 2016. STUDY DESIGN: We used data from a baseline period (discharges between January 1, 2010 and September 30, 2013) to predict spending in a performance period (discharges between October 1, 2015 and June 30, 2016). For 23 clinical episode types in BPCI-A, we compared the average prediction error across hospitals associated with each statistical approach. We also calculated an average across all clinical episode types and explored differences by hospital size. DATA COLLECTION/EXTRACTION METHODS: We used a 20% sample of Medicare claims, excluding hospitals and episode types with small numbers of observations. PRINCIPAL FINDINGS: The empirical Bayes approach resulted in significantly more accurate episode spending predictions for 19 of 23 clinical episode types. Across all episode types, prediction error averaged $8456 for the CMS approach versus $7521 for the empirical Bayes approach. Greater improvements in accuracy were observed with increasing hospital size. CONCLUSIONS: CMS should consider using empirical Bayes methods to calculate target prices for BPCI-A.