Volume 54 | Number 1 | February 2019

Abstract List

Lindsey M. Philpot PhD, MPH, Kristi M. Swanson MS, Jonathan Inselman MS, William J. Schoellkopf MS, James M. Naessens Sc.D., Bijan J. Borah PhD, Stephanie Peterson BS, Barbara Gladders MS, Nilay D. Shah Ph.D., Jon O. Ebbert MD, MSc


To evaluate the ability of claims‐based risk adjustment and incremental components of clinical data to identify 90‐day episode costs among lower extremity joint replacement () patients according to the Centers for Medicare & Medicaid Services () Comprehensive Care for Joint Replacement () program provisions.

Data Sources

Medicare fee‐for‐service () data for qualifying episodes in the United States, and data linked with clinical data from ‐qualifying episodes performed at High Value Healthcare Collaborative () and Mayo Clinic in 2013. and Mayo Clinic populations are subsets of the total population to assess the additive value of additional pieces of clinical data in correctly assigning patients to cost groups.

Study Design

Multivariable logistic models identified high‐cost episodes.

Data Collection/Extraction Methods

Clinical data from participating health care systems merged with Medicare data.

Principal Findings

Our three populations consisted of 363 621 patients in the population, 4881 in the population, and 918 in the Mayo population. When modeling per specifications, we observed low to moderate model performance (‐Stat = 0.714; ‐Stat = 0.628; Mayo ‐Stat = 0.587). Adding ‐ categories improved identification of patients in the top 20% of episode costs (‐Stat = 0.758, ‐Stat = 0.692, Mayo ‐Stat = 0.677). Clinical variables, particularly functional status in the population for which this was available (Mayo ‐Stat = 0.783), improved ability to identify patients within cost groups.


Policy makers could use these findings to improve payment adjustments for bundled procedures and in consideration of new data elements for reimbursement.