Identifying high‐cost episodes in lower extremity joint replacement

Abstract

Objectives 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 (LEJR) patients according to the Centers for Medicare & Medicaid Services (CMS) Comprehensive Care for Joint Replacement (CJR) program provisions. Data Sources Medicare fee‐for‐service (FFS) data for qualifying CJR episodes in the United States, and FFS data linked with clinical data from CJR‐qualifying LEJR episodes performed at High Value Healthcare Collaborative (HVHC) and Mayo Clinic in 2013. HVHC and Mayo Clinic populations are subsets of the total FFS 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 FFS data. Principal Findings Our three populations consisted of 363 621 patients in the CMS population, 4881 in the HVHC population, and 918 in the Mayo population. When modeling per CJR specifications, we observed low to moderate model performance (CMS C‐Stat = 0.714; HVHC C‐Stat = 0.628; Mayo C‐Stat = 0.587). Adding CMS‐HCC categories improved identification of patients in the top 20% of episode costs (CMS C‐Stat = 0.758, HVHC C‐Stat = 0.692, Mayo C‐Stat = 0.677). Clinical variables, particularly functional status in the population for which this was available (Mayo C‐Stat = 0.783), improved ability to identify patients within cost groups. Conclusions Policy makers could use these findings to improve payment adjustments for bundled LEJR procedures and in consideration of new data elements for reimbursement.

Does a Modified Frailty Index Predict 30-day Complications After Long-Bone Nonunion or Malunion Surgery?

“…2,3 Accurate risk stratification of patients is essential given the high cost of operative treatment of fracture nonunions/malunions along with the increased trend toward Medicare bundled payment models and merit-based incentive payments. [4][5][6] Frailty is described as deterioration in physiological functioning beyond that attributable to age-related changes alone and has been found to be a more robust predictor of surgical outcomes than American Society of Anesthesiology (ASA) class and age in several studies. [7][8][9][10] There have been 2 main methods of assessing frailty, either operationalizing frailty as the number of comorbidities an individual has in the frailty index model or considering frailty as a syndrome manifested by motor symptoms and unintentional weight loss in the frailty phenotype model.…”

“…2,3 Accurate risk stratification of patients is essential given the high cost of operative treatment of fracture nonunions/malunions along with the increased trend toward Medicare bundled payment models and merit-based incentive payments. 4–6…”