Mathematics Department, The College of William & Mary, Williamsburg, VA, USA.
Health Care Manag Sci. 2011 Jun;14(2):158-73. doi: 10.1007/s10729-011-9153-z. Epub 2011 Mar 29.
We devise models and algorithms to estimate the impact of current and future patient demand for examinations on Magnetic Resonance Imaging (MRI) machines at a hospital radiology department. Our work helps improve scheduling decisions and supports MRI machine personnel and equipment planning decisions. Of particular novelty is our use of scheduling algorithms to compute the competing objectives of maximizing examination throughput and patient-magnet utilization. Using our algorithms retrospectively can help (1) assess prior scheduling decisions, (2) identify potential areas of efficiency improvement and (3) identify difficult examination types. Using a year of patient data and several years of MRI utilization data, we construct a simulation model to forecast MRI machine demand under a variety of scenarios. Under our predicted demand model, the throughput calculated by our algorithms acts as an estimate of the overtime MRI time required, and thus, can be used to help predict the impact of different trends in examination demand and to support MRI machine staffing and equipment planning.
我们设计模型和算法来评估当前和未来患者对医院放射科磁共振成像 (MRI) 机器检查的需求对 MRI 机器的影响。我们的工作有助于改善调度决策，并支持 MRI 机器人员和设备规划决策。特别新颖的是，我们使用调度算法来计算最大化检查吞吐量和患者磁体利用率的竞争目标。使用我们的算法进行回顾性分析可以帮助：（1）评估先前的调度决策，（2）确定潜在的效率改进领域，（3）识别困难的检查类型。我们使用一年的患者数据和几年的 MRI 使用数据，构建了一个模拟模型，以在各种情况下预测 MRI 机器的需求。在我们预测的需求模型下，我们算法计算的吞吐量充当了所需额外 MRI 时间的估计值，因此，可以用于帮助预测不同检查需求趋势的影响，并支持 MRI 机器人员配备和设备规划。
