Division of Laboratory and Genomic Medicine, Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA.
Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S. Kingshighway Blvd, Campus Box 8131, St. Louis, MO, 63110, USA.
J Digit Imaging. 2020 Feb;33(1):143-150. doi: 10.1007/s10278-019-00252-w.
Scheduling of CT and MR exams requires reasonable estimates for expected scan duration. However, scan-time variability and efficiency gains from combining multiple exams are not quantitatively well characterized. In this work, we developed an informatics approach to quantify typical duration, duration variability, and multiple-procedure efficiency on a large scale, and used the approach to analyze 48,766 CT- and MR-based neuroradiological exams performed over one year. We found MR exam durations demonstrated higher absolute variability, but lower relative variability and lower multiple-procedure efficiency, compared to CT exams (p < 0.001). Our approach enables quantification of real-world operational performance and variability to inform optimal patient scheduling, efficient resource utilization, and sustainable service planning.
CT 和 MR 检查的安排需要对预期扫描时长进行合理估计。然而，扫描时间的可变性以及通过组合多个检查来提高效率的情况并没有得到很好的量化描述。在这项工作中，我们开发了一种信息学方法，可以大规模地量化典型时长、时长可变性和多程序效率，并使用该方法分析了一年中进行的 48766 次基于 CT 和 MR 的神经放射学检查。我们发现与 CT 检查相比，MR 检查的持续时间表现出更高的绝对可变性，但相对可变性和多程序效率较低（p < 0.001）。我们的方法能够量化实际操作性能和可变性，从而为优化患者安排、有效利用资源和可持续服务规划提供信息。
