Benjamin A Y Cher1, Baris Gulseren2,3, Andrew M Ryan2,3. 1. University of Michigan Medical School, Ann Arbor, Michigan, USA. 2. University of Michigan School of Public Health, Ann Arbor, Michigan, USA. 3. Center for Evaluating Health Reform, Ann Arbor, Michigan, USA.
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.
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.
Authors: Jeffrey H Silber; Ville A Satopää; Nabanita Mukherjee; Veronika Rockova; Wei Wang; Alexander S Hill; Orit Even-Shoshan; Paul R Rosenbaum; Edward I George Journal: Health Serv Res Date: 2016-03-14 Impact factor: 3.402