Examining variation in Medicare payments and drivers of cost for carotid endarterectomy.

BACKGROUND: There is a growing interest in providing high quality and low-cost care to Americans. A pursuit exists to measure not only how well hospitals are performing but also at what cost. We examined the variation in costs associated with carotid endarterectomy (CEA), to determine which components contribute to the variation and what drives increased payments. MATERIALS AND METHODS: Patients undergoing CEA between 2009 and 2012 were identified in the Medicare provider and analysis review database. Hospital quintiles of cost were generated and variation examined. Multivariable logistic regression was performed to identify independent predictors of high-payment hospitals for both asymptomatic and symptomatic patients undergoing CEA. RESULTS: A total of 264,018 CEAs were performed between 2009 and 2012; 250,317 were performed in asymptomatic patients in 2302 hospitals and 13,701 in symptomatic patients in 1851 hospitals. Higher payment hospitals had a higher percentage of nonwhite patients and comorbidity burden. The largest contributors to variation in overall payments were diagnosis-related groups, postdischarge, and readmission payments. After accounting for clustering at the hospital level, independent predictors of high-payment hospitals for all patients were postoperative stroke, length of stay, and readmission ,whereas in the symptomatic group, additional drivers included yearly volume and serious complications. CONCLUSIONS: CEA Medicare payments vary nationwide with diagnosis-related group, readmission, and postdischarge payments being the largest contributors to overall payment variation. In addition, stroke, length of stay, and readmission were the only independent predictors of high payment for all patients undergoing CEA.

Hospital resource intensity and cirrhosis mortality in United States.

AIM: To determine whether hospital characteristics predict cirrhosis mortality and how much variation in mortality is attributable to hospital differences. METHODS: We used data from the 2005-2011 Nationwide Inpatient Sample and the American Hospital Association Annual survey to identify hospitalizations for decompensated cirrhosis and corresponding facility characteristics. We created hospital-specific risk and reliability-adjusted odds ratios for cirrhosis mortality, and evaluated patient and facility differences based on hospital performance quintiles. We used hierarchical regression models to determine the effect of these factors on mortality. RESULTS: Seventy-two thousand seven hundred and thirty-three cirrhosis admissions were evaluated in 805 hospitals. Hospital mean cirrhosis annual case volume was 90.4 (range 25-828). Overall hospital cirrhosis mortality rate was 8.00%. Hospital-adjusted odds ratios (aOR) for mortality ranged from 0.48 to 1.89. Patient characteristics varied significantly by hospital aOR for mortality. Length of stay averaged 6.0 ± 1.6 days, and varied significantly by hospital performance (P < 0.001). Facility level predictors of risk-adjusted mortality were higher Medicaid case-mix (OR = 1.00, P = 0.029) and LPN staffing (OR = 1.02, P = 0.015). Higher cirrhosis volume (OR = 0.99, P = 0.025) and liver transplant program status (OR = 0.83, P = 0.026) were significantly associated with survival. After adjusting for patient differences, era, and clustering effects, 15.3% of variation between hospitals was attributable to differences in facility characteristics. CONCLUSION: Hospital characteristics account for a significant proportion of variation in cirrhosis mortality. These findings have several implications for patients, providers, and health care delivery in liver disease care and inpatient health care design.

Understanding Inpatient Cost Variation in Kidney Transplantation: Implications for Payment Reforms.

OBJECTIVE: To examine the magnitude and sources of inpatient cost variation for kidney transplantation. METHODS: We used the 2005-2009 Nationwide Inpatient Sample to identify patients who underwent kidney transplantation. We first calculated the patient-level cost of each transplantation admission and then aggregated costs to the hospital level. We fit hierarchical linear regression models to identify sources of cost variation and to estimate how much unexplained variation remained after adjusting for case-mix variables commonly found in administrative datasets. RESULTS: We identified 8866 living donor (LDRT) and 5589 deceased donor (DDRT) renal transplantations. We found that higher costs were associated with the presence of complications (LDRT, 14%; P <.001; DDRT, 24%; P <.001), plasmapheresis (LDRT, 27%; P <.001; DDRT, 27%; P <.001), dialysis (LDRT, 4%; P <.001), and prolonged length of stay (LDRT, 84%; P <.001; DDRT, 82%; P <.001). Even after case-mix adjustment, a considerable amount of unexplained cost variation remained between transplant centers (DDRT, 52%; LDRT, 66%). CONCLUSION: Although significant inpatient cost variation is present across transplant centers, much of the cost variation for kidney transplantation is not explained by commonly used risk-adjustment variables in administrative datasets. These findings suggest that although there is an opportunity to achieve savings through payment reforms for kidney transplantation, policymakers should seek alternative sources of information (eg, clinical registry data) to delineate sources of warranted and unwarranted cost variation.