papers

Predicting healthcare costs in a population of Veterans Affairs beneficiaries using diagnosis-based risk adjustment and self-reported health status

Med Care 42:1027-1035.

Houston Center for Quality of Care and Utilization Studies, Health Services Research and Development Service, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA

BACKGROUND: Many healthcare organizations use diagnosis-based risk adjustment systems for predicting costs. Health self-report may add information not contained in a diagnosis-based system but is subject to incomplete response.

OBJECTIVE: The objective of this study was to evaluate the added predictive power of health self-report in combination with a diagnosis-based risk adjustment system in concurrent and prospective models of healthcare cost.

RESEARCH DESIGN: This was a cohort study using Department of Veterans Affairs (VA) administrative databases. We tested the predictive ability of the Adjusted Clinical Group (ACG) methodology and the added value of SF-36V (short form functional status for veterans) results. Linear regression models were compared using R(2), mean absolute prediction error (MAPE), and predictive ratio.

SUBJECTS: Subjects were 35,337 VA beneficiaries at 8 VA medical centers during fiscal year (FY) 1998 who voluntarily completed an SF-36V survey.

MEASURES: Outcomes were total FY 1998 and FY 1999 cost. Demographics and ACG-based Adjusted Diagnostic Groups (ADGs) with and without 8 SF-36V multiitem scales and the Physical Component Score and Mental Component Score were compared.

RESULTS: The survey response rate was 45%. Adding the 8 scales to ADGs and demographics increased the crossvalidated R by 0.007 in the prospective model. The 8 scales reduced the MAPE by 236 US dollars among patients in the upper 10% of FY 1999 cost.

CONCLUSIONS: The limited added predictive power of health self-report to a diagnosis-based risk adjustment system should be weighed against the cost of collecting these data. Adding health self-report data may increase predictive accuracy in high-cost patients.

PMID: 15377936