The performance of administrative and self-reported measures for risk adjustment of Veterans Affairs expenditures.

Published

Journal Article

OBJECTIVE: To evaluate the performance of different prospective risk adjustment models of outpatient, inpatient, and total expenditures of veterans who regularly use Veterans Affairs (VA) primary care. DATA SOURCES: We utilized administrative, survey and expenditure data on 14,449 VA patients enrolled in a randomized trial that gave providers regular patient health assessments. STUDY DESIGN: This cohort study compared five administrative data-based, two self-report risk adjusters, and base year expenditures in prospective models. DATA EXTRACTION METHODS: VA outpatient care and nonacute inpatient care expenditures were based on unit expenditures and utilization, while VA expenditures for acute inpatient care were calculated from a Medicare-based inpatient cost function. Risk adjusters for this sample were constructed from diagnosis, medication and self-report data collected during a clinical trial. Model performance was compared using adjusted R2 and predictive ratios. PRINCIPAL FINDINGS: In all expenditure models, administrative-based measures performed better than self-reported measures, which performed better than age and gender. The Diagnosis Cost Groups (DCG) model explained total expenditure variation (R2=7.2 percent) better than other models. Prior outpatient expenditures predicted outpatient expenditures best by far (R2=42 percent). Models with multiple measures improved overall prediction, reduced over-prediction of low expenditure quintiles, and reduced under-prediction in the highest quintile of expenditures. CONCLUSIONS: Prediction of VA total expenditures was poor because expenditure variation reflected utilization variation, but not patient severity. Base year expenditures were the best predictor of outpatient expenditures and nearly the best for total expenditures. Models that combined two or more risk adjusters predicted expenditures better than single-measure models, but are more difficult and expensive to apply.

Full Text

Duke Authors

Cited Authors

  • Maciejewski, ML; Liu, C-F; Derleth, A; McDonell, M; Anderson, S; Fihn, SD

Published Date

  • June 2005

Published In

Volume / Issue

  • 40 / 3

Start / End Page

  • 887 - 904

PubMed ID

  • 15960696

Pubmed Central ID

  • 15960696

Electronic International Standard Serial Number (EISSN)

  • 1475-6773

International Standard Serial Number (ISSN)

  • 0017-9124

Digital Object Identifier (DOI)

  • 10.1111/j.1475-6773.2005.00390.x

Language

  • eng