Department of Quality Assurance and Process Innovation, Academic Medical Center, Meibergdreef 9 Room D01-319, P.O. Box 22660, 1100 DD, Amsterdam, The Netherlands.
Health Care Manag Sci. 2011 Dec;14(4):348-60. doi: 10.1007/s10729-011-9168-5. Epub 2011 Jun 4.
An interactive tool was developed for the ophthalmology department of the Academic Medical Center to quantitatively support management with strategic patient-mix decisions. The tool enables management to alter the number of patients in various patient groups and to see the consequences in terms of key performance indicators. In our case study, we focused on the bottleneck: the operating room. First, we performed a literature review to identify all factors that influence an operating room's utilization rate. Next, we decided which factors were relevant to our study. For these relevant factors, two quantitative methods were applied to quantify the impact of an individual factor: regression analysis and computer simulation. Finally, the average duration of an operation, the number of cancellations due to overrun of previous surgeries, and the waiting time target for elective patients all turned out to have significant impact. Accordingly, for the case study, the interactive tool was shown to offer management quantitative decision support to act proactively to expected alterations in patient-mix. Hence, management can anticipate the future situation, and either alter the expected patient-mix or expand capacity to ensure that the key performance indicators will be met in the future.
一个互动工具被开发出来，用于学术医学中心的眼科部门，以定量支持管理层进行战略性的患者组合决策。该工具使管理层能够改变不同患者群体的患者数量，并从关键绩效指标的角度看到这些变化的结果。在我们的案例研究中，我们专注于瓶颈：手术室。首先，我们进行了文献回顾，以确定影响手术室利用率的所有因素。接下来，我们决定哪些因素与我们的研究有关。对于这些相关因素，我们应用了两种定量方法来量化单个因素的影响：回归分析和计算机模拟。最后，手术的平均持续时间、由于前一次手术超时而取消的手术数量以及择期患者的等待时间目标都被证明具有显著影响。因此，对于案例研究，该互动工具被证明为管理层提供了定量决策支持，以便主动应对患者组合的预期变化。因此，管理层可以预测未来的情况，要么改变预期的患者组合，要么扩大产能，以确保未来满足关键绩效指标。
