Development and Validation of an Algorithm to Identify Planned Readmissions From Claims Data.

BACKGROUND: It is desirable not to include planned readmissions in readmission measures because they represent deliberate, scheduled care. OBJECTIVES: To develop an algorithm to identify planned readmissions, describe its performance characteristics, and identify improvements. DESIGN: Consensus-driven algorithm development and chart review validation study at 7 acute-care hospitals in 2 health systems. PATIENTS: For development, all discharges qualifying for the publicly reported hospital-wide readmission measure. For validation, all qualifying same-hospital readmissions that were characterized by the algorithm as planned, and a random sampling of same-hospital readmissions that were characterized as unplanned. MEASUREMENTS: We calculated weighted sensitivity and specificity, and positive and negative predictive values of the algorithm (version 2.1), compared to gold standard chart review. RESULTS: In consultation with 27 experts, we developed an algorithm that characterizes 7.8% of readmissions as planned. For validation we reviewed 634 readmissions. The weighted sensitivity of the algorithm was 45.1% overall, 50.9% in large teaching centers and 40.2% in smaller community hospitals. The weighted specificity was 95.9%, positive predictive value was 51.6%, and negative predictive value was 94.7%. We identified 4 minor changes to improve algorithm performance. The revised algorithm had a weighted sensitivity 49.8% (57.1% at large hospitals), weighted specificity 96.5%, positive predictive value 58.7%, and negative predictive value 94.5%. Positive predictive value was poor for the 2 most common potentially planned procedures: diagnostic cardiac catheterization (25%) and procedures involving cardiac devices (33%). CONCLUSIONS: An administrative claims-based algorithm to identify planned readmissions is feasible and can facilitate public reporting of primarily unplanned readmissions.

An administrative claims measure of payments made for Medicare patients for a 30-day episode of care for acute myocardial infarction.

BACKGROUND: Understanding both cost and quality across institutions is a critical first step to illuminating the value of care purchased by Medicare. Under contract with the Centers for Medicare and Medicaid Services, we developed a method for profiling hospitals by 30-day episode-of-care costs (payments for Medicare beneficiaries) for acute myocardial infarction (AMI). METHODS: We developed a hierarchical generalized linear regression model to calculate hospital risk-standardized payment (RSP) for a 30-day episode for AMI. Using 2008 Medicare claims, we identified hospitalizations for patients 65 years of age or older with a discharge diagnosis of ICD-9 codes 410.xx. We defined an AMI episode as the date of admission plus 30 days. To reflect clinical care, we omitted or averaged payment adjustments for geographic factors and policy initiatives. We risk-adjusted for clinical variables identified in the 12 months preceding and including the AMI hospitalization. Using combined 2008-2009 data, we assessed measure reliability using an intraclass correlation coefficient and calculated the final RSP. RESULTS: The final model included 30 variables and resulted in predictive ratios (average predicted payment/average total payment) close to 1. The intraclass correlation coefficient score was 0.79. Across 2382 hospitals with ≥ 25 hospitalizations, the unadjusted mean payment was $20,324 ranging from $11,089 to $41,897. The mean RSP was $21,125 ranging from $13,909 to $28,979. CONCLUSIONS: This study introduces a claims-based measure of RSP for an AMI 30-day episode of care. The RSP varies among hospitals, with a 2-fold range in payments. When combined with quality measures, this payment measure will help profile high-value care.