title: Risk Adjustment Tools for Learning Health Systems: A Comparison of DxCG and CMS-HCC V21.
abstract: OBJECTIVE
  To compare risk scores computed by DxCG (Verisk) and Centers for Medicare and Medicaid Services (CMS) V21.
  
  
  METHODS
  Analysis of administrative data from the Department of Veterans Affairs (VA) for fiscal years 2010 and 2011.
  
  
  METHODS
  We regressed total annual VA costs on predicted risk scores. Model fit was judged by R-squared, root mean squared error, mean absolute error, and Hosmer-Lemeshow goodness-of-fit tests. Recalibrated models were tested using split samples with pharmacy data.
  
  
  METHODS
  We created six analytical files: a random sample (n = 2 million), high cost users (n = 261,487), users over age 75 (n = 644,524), mental health and substance use users (n = 830,832), multimorbid users (n = 817,951), and low-risk users (n = 78,032).
  
  
  RESULTS
  The DxCG Medicaid with pharmacy risk score yielded substantial gains in fit over the V21 model. Recalibrating the V21 model using VA pharmacy data-generated risk scores with similar fit statistics to the DxCG risk scores.
  
  
  CONCLUSIONS
  Although the CMS V21 and DxCG prospective risk scores were similar, the DxCG model with pharmacy data offered improved fit over V21. However, health care systems, such as the VA, can recalibrate the V21 model with additional variables to develop a tailored risk score that compares favorably to the DxCG models.
authors.affiliation: Health Economics Resource Center (HERC), VA Palo Alto, Menlo Park, CA. todd.wagner@va.gov.|Center for Innovation to Implementation, VA Palo Alto, Menlo Park, CA. todd.wagner@va.gov.|Department of Health Research and Policy, Stanford University, Palo Alto, CA. todd.wagner@va.gov.
authors.forename: Todd H
authors.initials: TH
authors.lastname: Wagner
authors.affiliation: Health Economics Resource Center (HERC), VA Palo Alto, Menlo Park, CA.
authors.forename: Anjali
authors.initials: A
authors.lastname: Upadhyay
authors.affiliation: Health Economics Resource Center (HERC), VA Palo Alto, Menlo Park, CA.
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Cowgill
authors.affiliation: VHA Office of Productivity, Efficiency & Staffing, Bedford, MA.
authors.forename: Theodore
authors.initials: T
authors.lastname: Stefos
authors.affiliation: VHA Office of Productivity, Efficiency & Staffing, Bedford, MA.
authors.forename: Eileen
authors.initials: E
authors.lastname: Moran
authors.affiliation: Center for Innovation to Implementation, VA Palo Alto, Menlo Park, CA.|Division of General Medical Disciplines, Stanford University, Palo Alto, CA.
authors.forename: Steven M
authors.initials: SM
authors.lastname: Asch
authors.affiliation: Department of Veterans Affairs, Operational Analytics and Reporting, Office of Informatics and Analytics, Kansas City, MO.|Department of Veterans Affairs, Office of Secretary, Kansas City, MO.|Department of Veterans Affairs, Center of Innovation, Kansas City, MO.|Vijay Babu Rayudu Endowed Chair in Patient Safety, University of Missouri-Kansas City, Kansas City, MO.
authors.forename: Peter
authors.initials: P
authors.lastname: Almenoff
mesh_terms: D000067575:Administrative Claims, Healthcare / Q000191:economics; D014487:Centers for Medicare and Medicaid Services, U.S.; D016303:Clinical Pharmacy Information Systems; D017048:Health Care Costs; D006801:Humans; D017059:Models, Econometric; D011446:Prospective Studies; D020379:Risk Adjustment / Q000379:methods*; D014481:United States; D014493:United States Department of Veterans Affairs
keywords: Risk adjustment; cost; health economics; performance measurement
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