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Objective: To determine the extent to which counting observation stays changes hospital performance on 30-day readmission measures., Methods: This was a retrospective study of inpatient admissions and observation stays among fee-for-service Medicare enrollees in 2017. We generated 3 specifications of 30-day risk-standardized readmissions measures: the hospital-wide readmission (HWR) measure utilized by the Centers for Medicare and Medicaid Services, which captures inpatient readmissions within 30 days of inpatient discharge; an expanded HWR measure, which captures any unplanned hospitalization (inpatient admission or observation stay) within 30 days of inpatient discharge; an all-hospitalization readmission (AHR) measure, which captures any unplanned hospitalization following any hospital discharge (observation stays are included in both the numerator and denominator of the measure). Estimated excess readmissions for hospitals were compared across the 3 measures. High performers were defined as those with a lower-than-expected number of readmissions whereas low performers had higher-than-expected or excess readmissions. Multivariable logistic regression identified hospital characteristics associated with worse performance under the measures that included observation stays., Results: Our sample had 2586 hospitals with 5,749,779 hospitalizations. Observation stays ranged from 0% to 41.7% of total hospitalizations. Mean (SD) readmission rates were 16.6% (5.4) for the HWR, 18.5% (5.7) for the expanded HWR, and 17.9% (5.7) in the all-hospitalization readmission measure. Approximately 1 in 7 hospitals (14.9%) would switch from being classified as a high performer to a low performer or vice-versa if observation stays were fully included in the calculation of readmission rates. Safety-net hospitals and those with a higher propensity to use observation would perform significantly worse., Conclusions: Fully incorporating observation stays in readmission measures would substantially change performance in value-based programs for safety-net hospitals and hospitals with high rates of observation stays., Competing Interests: The authors declare no conflict of interest., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Colonoscopy outcomes, such as polyp detection or complication rates, may differ by procedure indication., Objectives: To develop methods to classify colonoscopy indications from administrative data, facilitating study of colonoscopy quality and outcomes., Research Design: We linked 14,844 colonoscopy reports from the Clinical Outcomes Research Initiative, a national repository of endoscopic reports, to the corresponding Medicare Carrier and Outpatient File claims. Colonoscopy indication was determined from the procedure reports. We developed algorithms using classification and regression trees and linear discriminant analysis (LDA) to classify colonoscopy indication. Predictor variables included ICD-9CM and CPT/HCPCS codes present on the colonoscopy claim or in the 12 months prior, patient demographics, and site of colonoscopy service. Algorithms were developed on a training set of 7515 procedures, then validated using a test set of 7329 procedures., Results: Sensitivity was lowest for identifying average-risk screening colonoscopies, varying between 55% and 86% for the different algorithms, but specificity for this indication was consistently over 95%. Sensitivity for diagnostic colonoscopy varied between 77% and 89%, with specificity between 55% and 87%. Algorithms with classification and regression trees with 7 variables or LDA with 10 variables had similar overall accuracy, and generally lower accuracy than the algorithm using LDA with 30 variables., Conclusions: Algorithms using Medicare claims data have moderate sensitivity and specificity for colonoscopy indication, and will be useful for studying colonoscopy quality in this population. Further validation may be needed before use in alternative populations.