Target Prices Influence Hospital Participation And Shared Savings In Medicare Bundled Payment Program

Improving target price calculations in Medicare bundled payment programs

“…The primary advantage of the using the empirical Bayes approach for BPCI-A is that it addresses the issue that hospitals with high target prices may join the program and experience unwarranted financial gains through regression to the mean. 6 More accurate target prices could also address issues such as low participation rates, 23,24 high drop-out rates, 23,24 inequitable distribution of risk-sharing, 25 and substantial differences in hospital characteristics between participants and non-participants. 26,27 Savings associated with BPCI-A have been modest 1,28 in prior years; lower target prices resulting from empirical Bayes estimation would further encourage hospitals to lower spending and achieve shared savings with CMS.…”

“…Both the Hospital Readmission Reductions Program 21 and Hospital Compare 22 use Bayesian Shrinkage to profile hospital readmission and mortality rates. The primary advantage of the using the empirical Bayes approach for BPCI‐A is that it addresses the issue that hospitals with high target prices may join the program and experience unwarranted financial gains through regression to the mean 6 . More accurate target prices could also address issues such as low participation rates, 23,24 high drop‐out rates, 23,24 inequitable distribution of risk‐sharing, 25 and substantial differences in hospital characteristics between participants and non‐participants 26,27 .…”

“…[4][5][6] Hospital spending is susceptible to a statistical phenomenon known as regression to the mean, where hospital spending that is unusually high in a particular year is likely to decrease in the following years, and hospital spending that is unusually low in a particular year is likely to increase in the following years. 6 In essence, random noise can obscure policymakers' ability to observe hospitals' true spending performance. Evaluating hospitals' expected spending trends, and incorporating them into predictions, is another challenge.…”

“…Predicting provider spending, while necessary for alternative payment models, is challenging 4‐6 . Hospital spending is susceptible to a statistical phenomenon known as regression to the mean, where hospital spending that is unusually high in a particular year is likely to decrease in the following years, and hospital spending that is unusually low in a particular year is likely to increase in the following years 6 . In essence, random noise can obscure policymakers' ability to observe hospitals' true spending performance.…”

Association of Hospital Participation in Bundled Payments for Care Improvement Advanced With Medicare Spending and Hospital Incentive Payments

“…We first calculated target prices for each condition in which each hospital chose to participate. To do so, we followed CMS’s methodology and estimated the following: (1) a compound lognormal regression model of hospitals’ historical 90-day episode spending for discharges between January 1, 2013, and December 31, 2016, which included patient case-mix and hospital characteristics; and (2) a linear model of the spending trend for peer groups of hospitals with similar characteristics . Per CMS methods, case-mix was captured using age, comorbidities (via Hierarchical Condition Categories), disability as the reason for Medicare entitlement, and dual eligibility for Medicare and Medicaid.…”