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Using Diagnoses to Describe Populations and Predict Costs

The Diagnostic Cost Group Hierarchical Condition Category (DCG/HCC) payment models summarize the health care problems and predict the future health care costs of populations. These models use the diagnoses generated during patient encounters with the medical delivery system to infer which medical pr...

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Detalles Bibliográficos
Autores principales: Ash, Arlene S., Ellis, Randall P., Pope, Gregory C., Ayanian, John Z., Bates, David W., Burstin, Helen, Iezzoni, Lisa I., MacKay, Elizabeth, Yu, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: CENTERS for MEDICARE & MEDICAID SERVICES 2000
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4194673/
https://www.ncbi.nlm.nih.gov/pubmed/11481769
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author Ash, Arlene S.
Ellis, Randall P.
Pope, Gregory C.
Ayanian, John Z.
Bates, David W.
Burstin, Helen
Iezzoni, Lisa I.
MacKay, Elizabeth
Yu, Wei
author_facet Ash, Arlene S.
Ellis, Randall P.
Pope, Gregory C.
Ayanian, John Z.
Bates, David W.
Burstin, Helen
Iezzoni, Lisa I.
MacKay, Elizabeth
Yu, Wei
author_sort Ash, Arlene S.
collection PubMed
description The Diagnostic Cost Group Hierarchical Condition Category (DCG/HCC) payment models summarize the health care problems and predict the future health care costs of populations. These models use the diagnoses generated during patient encounters with the medical delivery system to infer which medical problems are present. Patient demographics and diagnostic profiles are, in turn, used to predict costs. We describe the logic, structure, coefficients and performance of DCG/HCC models, as developed and validated on three important data bases (privately insured, Medicaid, and Medicare) with more than 1 million people each.
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spelling pubmed-41946732014-11-04 Using Diagnoses to Describe Populations and Predict Costs Ash, Arlene S. Ellis, Randall P. Pope, Gregory C. Ayanian, John Z. Bates, David W. Burstin, Helen Iezzoni, Lisa I. MacKay, Elizabeth Yu, Wei Health Care Financ Rev Research Article The Diagnostic Cost Group Hierarchical Condition Category (DCG/HCC) payment models summarize the health care problems and predict the future health care costs of populations. These models use the diagnoses generated during patient encounters with the medical delivery system to infer which medical problems are present. Patient demographics and diagnostic profiles are, in turn, used to predict costs. We describe the logic, structure, coefficients and performance of DCG/HCC models, as developed and validated on three important data bases (privately insured, Medicaid, and Medicare) with more than 1 million people each. CENTERS for MEDICARE & MEDICAID SERVICES 2000 /pmc/articles/PMC4194673/ /pubmed/11481769 Text en
spellingShingle Research Article
Ash, Arlene S.
Ellis, Randall P.
Pope, Gregory C.
Ayanian, John Z.
Bates, David W.
Burstin, Helen
Iezzoni, Lisa I.
MacKay, Elizabeth
Yu, Wei
Using Diagnoses to Describe Populations and Predict Costs
title Using Diagnoses to Describe Populations and Predict Costs
title_full Using Diagnoses to Describe Populations and Predict Costs
title_fullStr Using Diagnoses to Describe Populations and Predict Costs
title_full_unstemmed Using Diagnoses to Describe Populations and Predict Costs
title_short Using Diagnoses to Describe Populations and Predict Costs
title_sort using diagnoses to describe populations and predict costs
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4194673/
https://www.ncbi.nlm.nih.gov/pubmed/11481769
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