The performance of administrative and self-reported measures for risk adjustment of veterans affairs expenditures

Health Services Research, June, 2005 by Matthew L. Maciejewski, Chuan-Fen Liu, Ann Derleth, Mary McDonell, Steve Anderson, Stephan D. Fihn

DISCUSSION

The purpose of this analysis was to compare the performance of different risk adjustment measures and examine predictive ability for low and high expenditure veterans receiving primary care from VA. Accurate prospective risk adjustment is desirable because failure to allocate resources properly can generate biased treatment effects in observational studies and over- or underpayment for certain types of patients. The VA budget allocation system to distribute congressional approved funds to VA networks is based on a capitation methodology rather than risk-adjusted payments. As a result, over-payment for low expenditure veterans creates incentives for VAMCs to attract healthy veterans to enroll. VAMCs have the incentive to minimize expenditures if they are under-paid for treating high risk, high expenditure veterans. In addition, VAMCs have an incentive to find ways to identify and attract low risk, low expenditure patients for which they are over-paid.

We found that adjustment strategies utilizing diagnostic and pharmacy information predicted total, inpatient and outpatient expenditures better than self-reported measures. No model predicted inpatient expenditures well, largely because only 17 percent of the sample was hospitalized. Estimates for outpatient expenditures were more stable and were predicted more accurately. Baseline outpatient expenditures predicted prospective outpatient expenditures vastly better than any other measure ([R.sup.2] = 41.9 percent). Prior year expenditures are not a practical risk adjuster for payment setting because of the implicit incentive to obtain higher payments in the next year by generating higher expenditures in the current year. Models that combined risk adjustment measures performed slightly better than single-measure models for all expenditures.

Prediction of VA total expenditures was two to three times lower than the best models used in general populations (Fowles et al. 1996; Pope et al. 1998; Ash et al. 2000; Rosen et al. 2001; Shen and Ellis 2002). Given a wide array of available risk adjustment measures, it is surprising that prospective total expenditure models did not approach 20 percent, the likely asymptote for [R.sup.2] in risk adjustment models (Newhouse et al. 1989). The poor prediction in this sample of VA primary care users compared with previous general population studies may be because of two factors. First, the unit cost approach in this analysis to estimate VA expenditures for outpatient and nonacute inpatient utilization does not vary by visit length. Accordingly, the attributed expenditures were not directly related to patient severity of illness, as they are in other health care systems that use pricing based expenditure systems. VA generates most of its revenue through Congressional allocation, so expenditures are calculated to track how this revenue allocation is used within and across VAMCs. A new VA cost-accounting system generates expenditures that do vary by visit duration, but these data were not available at the time of the clinical trial. If visit-level expenditures varied to indirectly reflect case complexity, the correlation between expenditures and risk (the numerator of [R.sup.2]) might have been higher.