BACKGROUND: Academic health centers continue their mission of clinical care, education, and research. This mission predisposes them to accept patients regardless of their individual clinical variation and financial risk. The purpose of this study is to assess the variation in costs and the attendant financial risk associated with these patients. In addition, we propose a new reimbursement methodology for academic health center high-end DRGs that better aligns financial risks. STUDY DESIGN: We reviewed clinical and financial data from the University of Michigan data warehouse for FY1999 (n = 39,804). The diagnosis-related groups were classified by volume (group 1, low volume to group 4, high volume). The coefficient of variation for total cost per admission was then calculated for each DRG classification. A regression analysis was also performed to assess how costs in the first 3 days estimated total costs. A hybrid methodology to estimate costs was then determined and its accuracy benchmarked against actual Medicare and Blue Cross reimbursements. RESULTS: Low-volume DRGs (< 75 annual admissions) had the highest coefficient of variation relative to each of the three other DRG classifications (moderate to high volume, groups 2, 3, and 4). The regression analysis accurately estimated costs (within 25% of actual costs) in 64.7% of patients with a length of stay > or = 4 days (n = 16,287). This regression fared well compared with actual FY 1999 DRG-based Medicare and Blue Cross reimbursements (n = 9,085 with length of stay > or = 4 days), which accurately reimbursed the University of Michigan Health System in only 43.9% of cases. CONCLUSIONS: Academic health centers receive a disproportionate number of admissions to low-volume, high-variation DRGs. This clinical variation translates into financial risk. Traditional risk management strategies are difficult to use in health care settings. The application of our proposed reimbursement methodology better distributes risk between payers and providers, and reduces adverse selection and incentive problems ("moral hazard").
BACKGROUND: Academic health centers continue their mission of clinical care, education, and research. This mission predisposes them to accept patients regardless of their individual clinical variation and financial risk. The purpose of this study is to assess the variation in costs and the attendant financial risk associated with these patients. In addition, we propose a new reimbursement methodology for academic health center high-end DRGs that better aligns financial risks. STUDY DESIGN: We reviewed clinical and financial data from the University of Michigan data warehouse for FY1999 (n = 39,804). The diagnosis-related groups were classified by volume (group 1, low volume to group 4, high volume). The coefficient of variation for total cost per admission was then calculated for each DRG classification. A regression analysis was also performed to assess how costs in the first 3 days estimated total costs. A hybrid methodology to estimate costs was then determined and its accuracy benchmarked against actual Medicare and Blue Cross reimbursements. RESULTS: Low-volume DRGs (< 75 annual admissions) had the highest coefficient of variation relative to each of the three other DRG classifications (moderate to high volume, groups 2, 3, and 4). The regression analysis accurately estimated costs (within 25% of actual costs) in 64.7% of patients with a length of stay > or = 4 days (n = 16,287). This regression fared well compared with actual FY 1999 DRG-based Medicare and Blue Cross reimbursements (n = 9,085 with length of stay > or = 4 days), which accurately reimbursed the University of Michigan Health System in only 43.9% of cases. CONCLUSIONS: Academic health centers receive a disproportionate number of admissions to low-volume, high-variation DRGs. This clinical variation translates into financial risk. Traditional risk management strategies are difficult to use in health care settings. The application of our proposed reimbursement methodology better distributes risk between payers and providers, and reduces adverse selection and incentive problems ("moral hazard").
Authors: Stephen B Williams; Yong Shan; Usama Jazzar; Hemalkumar B Mehta; Jacques G Baillargeon; Jinhai Huo; Anthony J Senagore; Eduardo Orihuela; Douglas S Tyler; Todd A Swanson; Ashish M Kamat Journal: JAMA Surg Date: 2018-10-01 Impact factor: 14.766