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Risk-Adjusted Medicare Capitation Rates Using Ambulatory and Inpatient Diagnoses

Researchers at The Johns Hopkins University (JHU) developed two new diagnosis-oriented methodologies for setting risk-adjusted capitation rates for managed care plans contracting with Medicare. These adjusters predict the future medical expenditures of aged Medicare enrollees based on demographic fa...

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Autores principales: Weiner, Jonathan P., Dobson, Allen, Maxwell, Stephanie L., Coleman, Kevin, Starfield, Barbara H., Anderson, Gerard F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: CENTERS for MEDICARE & MEDICAID SERVICES 1996
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4193605/
https://www.ncbi.nlm.nih.gov/pubmed/10158737
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author Weiner, Jonathan P.
Dobson, Allen
Maxwell, Stephanie L.
Coleman, Kevin
Starfield, Barbara H.
Anderson, Gerard F.
author_facet Weiner, Jonathan P.
Dobson, Allen
Maxwell, Stephanie L.
Coleman, Kevin
Starfield, Barbara H.
Anderson, Gerard F.
author_sort Weiner, Jonathan P.
collection PubMed
description Researchers at The Johns Hopkins University (JHU) developed two new diagnosis-oriented methodologies for setting risk-adjusted capitation rates for managed care plans contracting with Medicare. These adjusters predict the future medical expenditures of aged Medicare enrollees based on demographic factors and diagnostic information. The models use the Ambulatory Care Group (ACG) algorithm to categorize ambulatory diagnoses. Two alternative approaches for categorizing inpatient diagnoses were used. Lewin-VHI, Inc. evaluated the models using data from 624,000 randomly selected aged Medicare beneficiaries. The models predict expenditures far better than the Adjusted Average per Capita Cost (AAPCC) payment method. It is possible that risk-adjusted capitation payments could encourage health plans to compete on the basis of efficiency and quality and not risk selection.
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spelling pubmed-41936052014-11-04 Risk-Adjusted Medicare Capitation Rates Using Ambulatory and Inpatient Diagnoses Weiner, Jonathan P. Dobson, Allen Maxwell, Stephanie L. Coleman, Kevin Starfield, Barbara H. Anderson, Gerard F. Health Care Financ Rev Managed Care: Advances in Financing Researchers at The Johns Hopkins University (JHU) developed two new diagnosis-oriented methodologies for setting risk-adjusted capitation rates for managed care plans contracting with Medicare. These adjusters predict the future medical expenditures of aged Medicare enrollees based on demographic factors and diagnostic information. The models use the Ambulatory Care Group (ACG) algorithm to categorize ambulatory diagnoses. Two alternative approaches for categorizing inpatient diagnoses were used. Lewin-VHI, Inc. evaluated the models using data from 624,000 randomly selected aged Medicare beneficiaries. The models predict expenditures far better than the Adjusted Average per Capita Cost (AAPCC) payment method. It is possible that risk-adjusted capitation payments could encourage health plans to compete on the basis of efficiency and quality and not risk selection. CENTERS for MEDICARE & MEDICAID SERVICES 1996 /pmc/articles/PMC4193605/ /pubmed/10158737 Text en
spellingShingle Managed Care: Advances in Financing
Weiner, Jonathan P.
Dobson, Allen
Maxwell, Stephanie L.
Coleman, Kevin
Starfield, Barbara H.
Anderson, Gerard F.
Risk-Adjusted Medicare Capitation Rates Using Ambulatory and Inpatient Diagnoses
title Risk-Adjusted Medicare Capitation Rates Using Ambulatory and Inpatient Diagnoses
title_full Risk-Adjusted Medicare Capitation Rates Using Ambulatory and Inpatient Diagnoses
title_fullStr Risk-Adjusted Medicare Capitation Rates Using Ambulatory and Inpatient Diagnoses
title_full_unstemmed Risk-Adjusted Medicare Capitation Rates Using Ambulatory and Inpatient Diagnoses
title_short Risk-Adjusted Medicare Capitation Rates Using Ambulatory and Inpatient Diagnoses
title_sort risk-adjusted medicare capitation rates using ambulatory and inpatient diagnoses
topic Managed Care: Advances in Financing
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4193605/
https://www.ncbi.nlm.nih.gov/pubmed/10158737
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