Creating a MEDPAR Analog to the RUG-III Classification System

As Medicare payments for post-acute institutional care continue to rise sharply, policy interest in the clinical characteristics of beneficiaries admitted to nursing homes and their variation across facilities has stimulated research into case mix. Measures of Medicare skilled nursing facility (SNF)...

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Autores principales: Cornelius, Elizabeth, Feldman, Janet, Marsteller, Jill A., Liu, Korbin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: CENTERS for MEDICARE & MEDICAID SERVICES 1994
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4193490/
https://www.ncbi.nlm.nih.gov/pubmed/10142367
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author Cornelius, Elizabeth
Feldman, Janet
Marsteller, Jill A.
Liu, Korbin
author_facet Cornelius, Elizabeth
Feldman, Janet
Marsteller, Jill A.
Liu, Korbin
author_sort Cornelius, Elizabeth
collection PubMed
description As Medicare payments for post-acute institutional care continue to rise sharply, policy interest in the clinical characteristics of beneficiaries admitted to nursing homes and their variation across facilities has stimulated research into case mix. Measures of Medicare skilled nursing facility (SNF) case mix are important in relating payments to the care requirements of residents. The Resource Utilization Groups, Version III (RUG-III) classification system uses a new minimum data set that is not currently available nationally. In preparation for a multi-State demonstration, we needed to simulate at least the first-level splits at the national, State, and facility level. Therefore, we developed proxy measures using comparable data available on the National Claims History files. The analog is an easily programmed measure of the acuity/severity of beneficiaries' conditions across a Medicare Part A SNF stay in 75 percent of the SNF providers. This can be a method for estimating changes in case mix over the years, and differences across provider types and States.
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spelling pubmed-41934902014-11-04 Creating a MEDPAR Analog to the RUG-III Classification System Cornelius, Elizabeth Feldman, Janet Marsteller, Jill A. Liu, Korbin Health Care Financ Rev Medicare Payment Systems: Moving Toward the Future As Medicare payments for post-acute institutional care continue to rise sharply, policy interest in the clinical characteristics of beneficiaries admitted to nursing homes and their variation across facilities has stimulated research into case mix. Measures of Medicare skilled nursing facility (SNF) case mix are important in relating payments to the care requirements of residents. The Resource Utilization Groups, Version III (RUG-III) classification system uses a new minimum data set that is not currently available nationally. In preparation for a multi-State demonstration, we needed to simulate at least the first-level splits at the national, State, and facility level. Therefore, we developed proxy measures using comparable data available on the National Claims History files. The analog is an easily programmed measure of the acuity/severity of beneficiaries' conditions across a Medicare Part A SNF stay in 75 percent of the SNF providers. This can be a method for estimating changes in case mix over the years, and differences across provider types and States. CENTERS for MEDICARE & MEDICAID SERVICES 1994 /pmc/articles/PMC4193490/ /pubmed/10142367 Text en
spellingShingle Medicare Payment Systems: Moving Toward the Future
Cornelius, Elizabeth
Feldman, Janet
Marsteller, Jill A.
Liu, Korbin
Creating a MEDPAR Analog to the RUG-III Classification System
title Creating a MEDPAR Analog to the RUG-III Classification System
title_full Creating a MEDPAR Analog to the RUG-III Classification System
title_fullStr Creating a MEDPAR Analog to the RUG-III Classification System
title_full_unstemmed Creating a MEDPAR Analog to the RUG-III Classification System
title_short Creating a MEDPAR Analog to the RUG-III Classification System
title_sort creating a medpar analog to the rug-iii classification system
topic Medicare Payment Systems: Moving Toward the Future
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4193490/
https://www.ncbi.nlm.nih.gov/pubmed/10142367
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