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Using diagnoses to describe populations and predict costs
Quantitative Health Sciences Publications and Presentations
  • Arlene S. Ash, University of Massachusetts Medical School
  • Randall P. Ellis, Boston University
  • Gregory C. Pope, Health Economics Research, Inc.
  • John Z. Ayanian, Harvard Medical School
  • David W. Bates, Harvard Medical School
  • Helen Burstin, Harvard Medical School
  • Lisa I. Iezzoni, Harvard Medical School
  • Elizabeth MacKay, University of Calgary
  • Wei Yu, Boston University
UMMS Affiliation
Department of Quantitative Health Sciences
Publication Date
Document Type
Adolescent; Adult; Aged; Child; Child, Preschool; Cost Allocation; Demography; Diagnosis-Related Groups; Eligibility Determination; Female; Health Expenditures; Humans; Infant; Male; Managed Care Programs; Medicaid; Medicare; Middle Aged; *Models, Econometric

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.

Health Care Financ Rev. 2000 Spring;21(3):7-28. Link to article on publisher's site
PubMed ID
Related Resources
Link to Article in PubMed
Citation Information
Arlene S. Ash, Randall P. Ellis, Gregory C. Pope, John Z. Ayanian, et al.. "Using diagnoses to describe populations and predict costs" Vol. 21 Iss. 3 (2001) ISSN: 0195-8631 (Linking)
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