Finance Modeling in the Delivery of Medical Care in Tertiary-Care Hospitals in the Department of Veterans Affairs

Autor: Priscilla Hartel, William R. Best, Eileen Pierce, Charles H. Andrus, Soraida Berrios-Guccione, Paul J. Romito, Kristine Johnson
Rok vydání: 2001
Předmět:
Zdroj: Journal of Surgical Research. 96:152-157
ISSN: 0022-4804
DOI: 10.1006/jsre.1999.5728
Popis: Background In the mid-1990s, the Department of Veterans Affairs (DVA) implemented the Veterans Equitable Resource Allocation (VERA), a new financial model developed to attempt to better distribute the approximately $18 billion annual budget among roughly 170 Veterans Administration Medical Centers (VAMCs). VERA is based on a Health Maintenance Organization (HMO) model. VERA provides reimbursement to each of the 22 regional Veterans Integrated Service Networks (VISNs), and subsequent VISN distribution to individual VAMCs is based on an individual medical center's enrollment of unique social security numbers (uniques). In HMO vocabulary these are individual "covered lives." Methods Currently available demographic and staffing information regarding the DVA's 23 tertiary hospital systems (Category 7 hospitals) on the KLF database (DVA Austin Data Base) and published information on the DVA website were reviewed. The following was obtained: (1) staffing information-physician and nurse full-time employment equivalent (FTEE) staffing; (2) patient demographics and hospital workload-facility uniques (u), outpatient facility uniques, average daily census (ADC), discharges, and outpatient clinic visits. The following staffing ratios were calculated for both physician and nursing: FTEE/(u/1000), FTEE/(discharges/1000), FTEE/(clinic visits/1000), FTEE/ADC. For all categories the means +/- SD were calculated and correlation 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 VERA 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, respectively. Correlation statistics of staffing versus unique SSNs demonstrated correlation coefficients of 0.46 and 0.59 with respect to physician and nurse staffing, respectively. On the other hand, when physician FTEE and nursing FTEE staffing were compared with VAMC workload parameters (total ADC, discharges, and outpatient visits), correlation coefficients were more consistent, 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 services) 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 skewed toward the sicker, older patient) shows decreased correlation when compared with VAMC workload model parameters.
Databáze: OpenAIRE
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