Predicting healthcare costs in a population of veterans affairs beneficiaries using diagnosis-based risk adjustment and self-reported health status.
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.


METHODS
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.


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


METHODS
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.
Pietz
Kenneth
K
Houston Center for Quality of Care and Utilization Studies, Health Services Research and Development Service, Michael E. DeBakey Veterans Affairs Medical Center, Houston, Texas 77030, USA. kpietz@bcm.tmc.edu
Ashton
Carol M
CM
McDonell
Mary
M
Wray
Nelda P
NP
D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D016009:Chi-Square Distribution; D015331:Cohort Studies; D003625:Data Collection; D003953:Diagnosis-Related Groups; D005260:Female; D017048:Health Care Costs; D006304:Health Status; D006801:Humans; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D020379:Risk Adjustment; D012737:Sex Factors; D011795:Surveys and Questionnaires