Do financial incentives in a globally budgeted healthcare payment system produce changes in the way patients are categorized? A five-year study.
OBJECTIVE
To assess the responses to financial incentives after a change in the payment system in a capitation-style healthcare payment system over a 5-year period.


METHODS
Cross-sectional and longitudinal examination of cost, utilization, and diagnostic data.


METHODS
Using Veterans Health Administration (VHA) administrative data on healthcare users between fiscal years 1998 and 2002, we calculated the proportion of new patients entering each of the payment classes, the illness burden of patients entering the payment classes, and the profitability index (a ratio of payment to costs) for each class suspected of gaming and each control class. Our main dependent variables of interest were the differences in the measures between each utilization-based class and each diagnosis-based class. We used 2 different analytic approaches to assess whether these differences increased or decreased over time.


RESULTS
No clear evidence of gaming behavior was present in our results. A few comparisons were significant, but they did not show a consistent pattern of responses to incentives. For example, 6 of 16 comparisons of profitability index were significant, but (contrary to the hypothesis) 4 of these had a negative value for the time parameter, indicating decreasing profitability in the utilization-based classes versus the diagnosis-based classes.


CONCLUSIONS
Although the payment system could be manipulated to increase payment to VHA networks, no such consistent gaming behavior was observed. More research is needed to better understand the effects of financial incentives in other healthcare payment systems.
Petersen
Laura A
LA
Division of Health Policy and Quality, Houston Center for Quality of Care and Utilization Studies, Houston VA MedicalCenter, and Section for Health Services Research, Baylor College of Medicine, Houston, Tex 77030, USA. laurap@bcm.tmc.edu
Urech
Tracy H
TH
Byrne
Margaret M
MM
Pietz
Kenneth
K
D000328:Adult; D000368:Aged; D003363:Cost Control / Q000191:economics; D003362:Cost-Benefit Analysis / Q000191:economics; D003430:Cross-Sectional Studies; D003953:Diagnosis-Related Groups / Q000191:economics* / Q000706:statistics & numerical data; D005260:Female; D015276:Health Care Rationing / Q000191:economics* / Q000706:statistics & numerical data; D006302:Health Services Research; D017721:Hospital Costs / Q000706:statistics & numerical data*; D006787:Hospitals, Veterans / Q000191:economics / Q000592:standards; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008485:Medical Audit / Q000706:statistics & numerical data; D008875:Middle Aged; D010343:Patient Admission / Q000191:economics* / Q000706:statistics & numerical data; D011785:Quality Assurance, Health Care / Q000191:economics* / Q000706:statistics & numerical data; D012044:Regression Analysis; D012052:Reimbursement, Incentive; D014481:United States / Q000453:epidemiology; D014493:United States Department of Veterans Affairs