Elise H Lawson1, David S Zingmond, Bruce Lee Hall, Rachel Louie, Robert H Brook, Clifford Y Ko. 1. *Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, CA †Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, IL ‡VA Greater Los Angeles Healthcare System, Los Angeles, CA §Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA ¶Department of Surgery, School of Medicine, Washington University in St Louis and Barnes Jewish Hospital, St Louis, MO; Center for Health Policy and the Olin Business School at Washington University in St Louis, St Louis, MO; and Department of Surgery, John Cochran Veterans Affairs Medical Center, St Louis, MO ‖RAND Corporation, Santa Monica, CA; and **UCLA Jonathan and Karin Fielding School of Public Health, Los Angeles, CA.
Abstract
OBJECTIVE: To compare the classification of hospital statistical outlier status as better or worse performance than expected for postoperative complications using Medicare claims versus clinical registry data. BACKGROUND: Controversy remains as to the most favorable data source for measuring postoperative complications for pay-for-performance and public reporting polices. METHODS: Patient-level records (2005-2008) were linked between the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) and Medicare inpatient claims. Hospital statistical outlier status for better or worse performance than expected was assessed using each data source for superficial surgical site infection (SSI), deep/organ-space SSI, any SSI, urinary tract infection, pneumonia, sepsis, deep venous thrombosis, pulmonary embolism, venous thromboembolism, and myocardial infarction by developing hierarchical multivariable logistic regression models. Kappa statistics and correlation coefficients assessed agreement between the data sources. RESULTS: A total of 192 hospitals with 110,987 surgical patients were included. Agreement on hospital rank for complication rates between Medicare claims and ACS-NSQIP was poor-to-moderate (weighted κ: 0.18-0.48). Of hospitals identified as statistical outliers for better or worse performance by Medicare claims, 26% were also identified as outliers by ACS-NSQIP. Of outliers identified by ACS-NSQIP, 16% were also identified as outliers by Medicare claims. Agreement between the data sources on hospital outlier status classification was uniformly poor (weighted κ: -0.02-0.34). CONCLUSIONS: Despite using the same statistical methodology with each data source, classification of hospital outlier status as better or worse performance than expected for postoperative complications differed substantially between ACS-NSQIP and Medicare claims.
OBJECTIVE: To compare the classification of hospital statistical outlier status as better or worse performance than expected for postoperative complications using Medicare claims versus clinical registry data. BACKGROUND: Controversy remains as to the most favorable data source for measuring postoperative complications for pay-for-performance and public reporting polices. METHODS:Patient-level records (2005-2008) were linked between the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) and Medicare inpatient claims. Hospital statistical outlier status for better or worse performance than expected was assessed using each data source for superficial surgical site infection (SSI), deep/organ-space SSI, any SSI, urinary tract infection, pneumonia, sepsis, deep venous thrombosis, pulmonary embolism, venous thromboembolism, and myocardial infarction by developing hierarchical multivariable logistic regression models. Kappa statistics and correlation coefficients assessed agreement between the data sources. RESULTS: A total of 192 hospitals with 110,987 surgical patients were included. Agreement on hospital rank for complication rates between Medicare claims and ACS-NSQIP was poor-to-moderate (weighted κ: 0.18-0.48). Of hospitals identified as statistical outliers for better or worse performance by Medicare claims, 26% were also identified as outliers by ACS-NSQIP. Of outliers identified by ACS-NSQIP, 16% were also identified as outliers by Medicare claims. Agreement between the data sources on hospital outlier status classification was uniformly poor (weighted κ: -0.02-0.34). CONCLUSIONS: Despite using the same statistical methodology with each data source, classification of hospital outlier status as better or worse performance than expected for postoperative complications differed substantially between ACS-NSQIP and Medicare claims.
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