Finance modeling in the delivery of medical care in tertiary-care hospitals in the Department of Veterans Affairs

yyBackground. In the mid-1990s, the Department of Veterans Affairs (DVA) im plemented the Veterans Equitable Resource Allocation (VERA), a new financia l model developed to attempt to better distribute the similar to $18 billio n annual budget among roughly 170 Veterans Administration Medical Centers ( VAMCs). VERA is based on a Health Maintenance Organization (HMO) model. VER A provides reimbursement to each of the 22 regional Veterans Integrated Ser vice Networks (VISNs), and subsequent VISN distribution to individual VAMCs is based on an individual medical center's enrollment of unique social sec urity numbers (uniques). In HMO vocabulary these are individual "covered li ves." Methods. Currently available demographic and staffing information regarding the DVA's 23 tertiary hospital systems (Category 7 hospitals) on the KLF d atabase (DVA Austin Data Base) and published information on the DVA website were reviewed. The following was obtained: (1) staffing information-physic ian and nurse full-time employment equivalent (FTEE) staffing; (2) patient demographics and hospital workload-facility uniques (u), outpatient facilit y uniques, average daily census (ADC), discharges, and outpatient clinic vi sits. The following staffing ratios were calculated for both physician and nursing: FTEE/ (u11000), FTEE/(discharges/1000), FTEE/(clinic visits/ 1000) , FTEE/ADC. For all categories the means +/- SD were calculated and correla tion coefficients were calculated on pertinent pairings. Results. Although categorized as similar tertiary care facilities, the 23 " Group 7" VA hospitals are anything but equivalent when reviewed using the V ERA financing model with respect to physician staffing, nurse staffing, and facility uniques. Using VERA methodology, average physician FTEE and total nursing FTEE staffing/(u/1000) are 3.67 +/- 0.89 and 15.53 +/- 3.77, respe ctively. Correlation statistics of staffing versus unique SSNs demonstrated correlation coefficients of 0.46 and 0.59 with respect to physician and nu rse staffing, respectively. On the other hand, when physician FTEE and nurs ing FTEE staffing were compared with VAMC workload parameters (total ADC, d ischarges, and outpatient visits), correlation coefficients were more consi stent, ranging from 0.62 to 0.86. Conclusions. In the VERA model, the reward of a larger annual budget for an individual VAMC or the regional VISN is realized when staffing of VAMCs is minimized, overall provided medical services (especially costly tertiary s ervices) are limited, and the number of covered lives is maximized. A VAMC staffing system that equates medical services delivered in a tertiary VAMC setting based on an HMO model like VERA (where the user population is skewe d toward the sicker, older patient) shows decreased correlation when compar ed with VAMC workload model parameters. (C) 2001 Academic Press.