University of Leipzig, Innovation Center Computer Assisted Surgery, Leipzig, Germany.
J Biomed Inform. 2013 Feb;46(1):152-9. doi: 10.1016/j.jbi.2012.10.002. Epub 2012 Oct 27.
Effective time and resource management in the operating room requires process information concerning the surgical procedure being performed. A major parameter relevant to the intraoperative process is the remaining intervention time. The work presented here describes an approach for the prediction of the remaining intervention time based on surgical low-level tasks.
A surgical process model optimized for time prediction was designed together with a prediction algorithm. The prediction accuracy was evaluated for two different neurosurgical interventions: discectomy and brain tumor resections. A repeated random sub-sampling validation study was conducted based on 20 recorded discectomies and 40 brain tumor resections.
The mean absolute error of the remaining intervention time predictions was 13 min 24s for discectomies and 29 min 20s for brain tumor removals. The error decreases as the intervention progresses.
The approach discussed allows for the on-line prediction of the remaining intervention time based on intraoperative information. The method is able to handle demanding and variable surgical procedures, such as brain tumor resections. A randomized study showed that prediction accuracies are reasonable for various clinical applications.
The predictions can be used by the OR staff, the technical infrastructure of the OR, and centralized management. The predictions also support intervention scheduling and resource management when resources are shared among different operating rooms, thereby reducing resource conflicts. The predictions could also contribute to the improvement of surgical workflow and patient care.
手术室中有效的时间和资源管理需要与正在进行的手术过程相关的流程信息。与手术过程相关的一个主要参数是剩余干预时间。本文介绍了一种基于手术低级别任务预测剩余干预时间的方法。
设计了一种针对时间预测优化的手术流程模型和预测算法。基于 20 例椎间盘切除术和 40 例脑肿瘤切除术，对两种不同的神经外科干预措施进行了预测准确性评估。采用重复随机子抽样验证研究。
椎间盘切除术的剩余干预时间预测平均绝对误差为 13 分 24 秒，脑肿瘤切除术为 29 分 20 秒。随着干预的进行，误差会逐渐减小。
本文讨论的方法允许基于术中信息在线预测剩余干预时间。该方法能够处理具有挑战性和多变的手术程序，如脑肿瘤切除术。随机研究表明，对于各种临床应用，预测精度是合理的。
手术室工作人员、手术室的技术基础设施和集中管理都可以使用这些预测结果。预测结果还支持在不同手术室之间共享资源时的干预调度和资源管理，从而减少资源冲突。这些预测结果还有助于改善手术流程和患者护理。
