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


METHODS
We utilized administrative, survey and expenditure data on 14,449 VA patients enrolled in a randomized trial that gave providers regular patient health assessments.


METHODS
This cohort study compared five administrative data-based, two self-report risk adjusters, and base year expenditures in prospective models.


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.


RESULTS
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.
Maciejewski
Matthew L
ML
Health Services Research and Development Northwest Center of Excellence, Veterans Affairs Puget Sound Health Care System, Seattle, WA, USA.
Liu
Chuan-Fen
CF
Derleth
Ann
A
McDonell
Mary
M
Anderson
Steve
S
Fihn
Stephan D
SD
D000368:Aged; D015331:Cohort Studies; D005260:Female; D019538:Health Care Surveys; D005102:Health Expenditures / Q000706:statistics & numerical data*; D006301:Health Services Needs and Demand / Q000639:trends; D006304:Health Status; D006787:Hospitals, Veterans / Q000191:economics / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D008875:Middle Aged; D011320:Primary Health Care / Q000191:economics / Q000706:statistics & numerical data*; D011446:Prospective Studies; D016032:Randomized Controlled Trials as Topic; D020379:Risk Adjustment / Q000379:methods*; D012650:Self Disclosure; D014481:United States; D014493:United States Department of Veterans Affairs; D014600:Utilization Review / Q000379:methods*