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Objective: The median time required to perform a surgical procedure is important in determining payment under Medicare's physician fee schedule. Prior studies have demonstrated that the current methodology of using physician surveys to determine surgical times results in overstated times. To measure surgical times more accurately, we developed and validated a methodology using available data from anesthesia billing data and operating room (OR) records.Data Sources: We estimated surgical times using Medicare 2011 anesthesia claims and New York Statewide Planning and Research Cooperative System 2011 OR times. Estimated times were validated using data from the National Surgical Quality Improvement Program. We compared our time estimates to those used by Medicare in the fee schedule.Study Design: We estimate surgical times via piecewise linear median regression models.Principal Findings: Using 3.0 million observations of anesthesia and OR times, we estimated surgical time for 921 procedures. Correlation between these time estimates and directly measured surgical time from the validation database was 0.98. Our estimates of surgical time were shorter than the Medicare fee schedule estimates for 78 percent of procedures.Conclusions: Anesthesia and OR times can be used to measure surgical time and thereby improve the payment for surgical procedures in the Medicare fee schedule. [ABSTRACT FROM AUTHOR]

To demonstrate how a Bayesian outlier accommodation model identifies and accommodates statistical outlier hospitals when developing facility payment adjustments for Medicare's prospective payment system for inpatient rehabilitation care.Administrative data on costs and facility characteristics of inpatient rehabilitation facilities (IRFs) for calendar years 1998 and 1999.Compare standard linear regression and the Bayesian outlier accommodation model for developing facility payment adjustors for a prospective payment system.Variables describing facility average cost per case and facility characteristics were derived from several administrative data sources.Evidence was found of non-normality of regression errors in the data used to develop facility payment adjustments for the inpatient rehabilitation facilities prospective payment system (IRF PPS). The Bayesian outlier accommodation model is shown to be appropriate for these data, but the model is largely consistent with the standard linear regression used in the development of the IRF PPS payment adjustors.The Bayesian outlier accommodation model is more robust to statistical outlier IRFs than standard linear regression for developing facility payment adjustments. It also allows for easy interpretation of model parameters, making it a viable policy alternative to standard regression in setting payment rates. [ABSTRACT FROM AUTHOR]