University Center for Health Sciences at Klinikum Augsburg (UNIKA-T), Neusässer Straße 47, 86156 Augsburg, Germany Chair of Health Care Operations/Health Information Management, Faculty of Business and Economics, University of Augsburg, Universitätsstraße 16, 86159, Augsburg, Germany.
Graduate Program in Operations Research and Industrial Engineering, The University of Texas at Austin, 204 E. Dean Keeton St. C2200, Austin, TX, 78712-1591, USA.
Health Care Manag Sci. 2020 Mar;23(1):80-101. doi: 10.1007/s10729-019-09476-2. Epub 2019 Feb 21.
This study analyzes the effect of economies of scale and scope on the optimal case mix of a hospital or hospital system. With respect to the ideal volume and patient composition, the goal is to evaluate (i) the impact of changes in the efficiency of resource use with increasing scale, and (ii) to determine the potential effects of spreading fixed costs over a greater number of patients. The problem is formulated as a non-linear mixed integer program. It turns out that this non-linear program is too difficult to be solved with standard software. As an alternative, an iterative procedure using piecewise linear approximations to derive lower and upper bounds is proposed and shown to converge to the optimum. The procedure is applied using a public database on German hospital costs and performance statistics. Results indicate that changes in the efficiency of resource use with increasing scale have a considerable impact if similar services can be consolidated, e.g., among different departments. However, if the scope for decision-making regarding the case mix of a hospital is limited, such changes may be negligible.
本研究分析了规模经济和范围经济对医院或医院系统最佳病例组合的影响。针对理想的容量和患者构成，目标是评估(i)随着规模的增加资源利用效率变化的影响，以及(ii)确定将固定成本分摊到更多患者身上的潜在影响。该问题被表述为一个非线性混合整数规划问题。结果表明，这个非线性规划问题太难了，无法用标准软件来解决。作为替代方案，提出了一种使用分段线性逼近来导出下界和上界的迭代过程，并证明其收敛于最优解。该过程使用德国医院成本和绩效统计的公共数据库进行了应用。结果表明，如果可以整合类似的服务，例如不同部门之间的服务，那么随着规模的增加资源利用效率的变化将产生相当大的影响。然而，如果医院病例组合决策的范围受到限制，那么这些变化可能微不足道。
