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Identifying and Accommodating Statistical Outliers When Setting Prospective Payment Rates for Inpatient Rehabilitation Facilities
Author(s) -
Paddock Susan M.,
Wynn Barbara O.,
Carter Grace M.,
Buntin Melinda Beeuwkes
Publication year - 2004
Publication title -
health services research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.706
H-Index - 121
eISSN - 1475-6773
pISSN - 0017-9124
DOI - 10.1111/j.1475-6773.2004.00322.x
Subject(s) - outlier , prospective payment system , linear regression , regression analysis , payment , data collection , bayesian probability , econometrics , computer science , statistics , machine learning , artificial intelligence , mathematics , world wide web
Objective. 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. Data Sources/Study Setting. Administrative data on costs and facility characteristics of inpatient rehabilitation facilities (IRFs) for calendar years 1998 and 1999. Study Design. Compare standard linear regression and the Bayesian outlier accommodation model for developing facility payment adjustors for a prospective payment system. Data Collection. Variables describing facility average cost per case and facility characteristics were derived from several administrative data sources. Principal Findings. 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. Conclusions. 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.