Impact of business infrastructure on financial metrics in departments of surgery

Background In the current environment, pressure is ever increasing to maximize financial performance in surgery departments. Factors such as physician extenders, billing and collection, payor mix, contracting, incentives from the Centers for Medicare and Medicaid Services, and administrative incentives may greatly influence financial performance. However, despite a plethora of information from the University HealthSystem Consortium and the Association of American Medical Colleges, best-practice information for business infrastructure is lacking. To obtain a sampling of current practices, we conducted a survey of departments of surgery. Methods An anonymous 30-question survey addressing demographics, productivity, revenue and expense profile, payor mix, physician extender and staff personnel, billing and collections methodology, and financial performance was distributed among members of the Society of Surgical Chairs via SurveyMonkey. This was approved by the Loyola Institutional Research Board. Multivariate linear regression analyses and t tests/rank-sum tests were performed, as appropriate. Data are presented as mean ± SEM. Results A total of 25 (19%) departments responded; 14 were integrated with the hospital/health system, and 11 were integrated with the medical school. In 60% ( n = 15), the main hospital had 500 to 1,000 beds; 48% ( n = 12) had >4 hospitals in their system. For FY10, MD clinical full-time equivalents (FTEs) were 49 ± 10; total work relative value units (wRVUs) were 320 ± 8 k; and total billed cases were 43 ± 16 k. A total of 23 of 25 used physician-extenders with an average of 18 ± 5 per department and in 22 of 23, the physician extenders billed. On average, there were 18 ± 6 clinical-support staff, 25 ± 11 front-office staff, and 13 ± 3 back-office support staff FTEs. Among these FTEs, there were 16 ± 5 devoted to business operations (billing, coding, denial/claims management, financial oversight). Collections/wRVUs were $60 ± 3 (range, 39–80). Regression modeling demonstrated that total wRVUs were determined by the number of MD FTEs ( P = .01), number of physician extenders ( P = .01), number of front-office staff ( P = .01), number of back-office staff ( P = .02), and number of total business staff ( P = .01). Collections/wRVUs were predicted by number of hospitals ( P = .04), number of MD FTEs ( P = .03), number of physician extenders ( P = .01), and number of cases/total business staff ( P = .02). Interestingly, wRVUs/MD was predi