Background-In 2009, the Centers for Medicare & Medicaid Services is publicly reporting hospital-level risk-standardized 30-day mortality and readmission rates after acute myocardial infarction (AMI) and heart failure (HF

Background National attention has increasingly focused on readmission as a target for quality improvement. We present the development and validation of a model approved by the National Quality Forum and used by the Centers for Medicare & Medicaid Services for hospital-level public reporting of risk-standardized readmission rates for patients discharged from the hospital after an acute myocardial infarction. Methods and Results We developed a hierarchical logistic regression model to calculate hospital risk-standardized 30-day all-cause readmission rates for patients hospitalized with acute myocardial infarction. The model was derived using Medicare claims data for a 2006 cohort and validated using claims and medical record data. The unadjusted readmission rate was 18.9%. The final model included 31 variables and had discrimination ranging from 8% observed 30-day readmission rate in the lowest predictive decile to 32% in the highest decile and a C statistic of 0.63. The 25th and 75th percentiles of the risk-standardized readmission rates across 3890 hospitals were 18.6% and 19.1%, with fifth and 95th percentiles of 18.0% and 19.9%, respectively. The odds of all-cause readmission for a hospital 1 SD above average were 1.35 times that of a hospital 1 SD below average. Hospital-level adjusted readmission rates developed using the claims model were similar to rates produced for the same cohort using a medical record model (correlation, 0.98; median difference, 0.02 percentage points). Conclusions This claims-based model of hospital risk-standardized readmission rates for patients with acute myocardial infarction produces estimates that are excellent surrogates for those produced from a medical record model.

Objectives:To compare the recording of 30-day postoperative complications between a national clinical registry and Medicare inpatient claims data and to determine whether the addition of outpatient claims data improves concordance with the clinical registry. Background: Policymakers are increasingly discussing use of postoperative complication rates for value-based purchasing. There is debate regarding the optimal data source for such measures. Methods: Patient records (2005)(2006)(2007)(2008) from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) were linked to Medicare inpatient and outpatient claims data sets. We assessed the ability of (1) Medicare inpatient claims and (2) Medicare inpatient and outpatient claims to detect a core set of ACS-NSQIP 30-day postoperative complications: superficial surgical site infection (SSI), deep/organ-space SSI, any SSI (superficial and/or deep/organ-space), urinary tract infection, pneumonia, sepsis, deep venous thrombosis (DVT), pulmonary embolism, venous thromboembolism (DVT and/or pulmonary embolism), and myocardial infarction. Agreement of patient-level complications by ACS-NSQIP versus Medicare was assessed by κ statistics. Results: A total of 117,752 patients from more than 200 hospitals were studied. The sensitivity of inpatient claims data for detecting ACS-NSQIP complications ranged from 0.27 to 0.78; the percentage of false-positives ranged from 48% to 84%. Addition of outpatient claims data improved sensitivity slightly but also greatly increased the percentage of false-positives. Agreement was routinely poor between clinical and claims data for patient-level complications.Conclusions: This analysis demonstrates important differences between ACS-NSQIP and Medicare claims data sets for measuring surgical complications. Poor accuracy potentially makes claims data suboptimal for evaluating surgical complications. These findings have meaningful implications for performance measures currently being considered.From the