Department of Engineering Science, The University of Auckland, Auckland, New Zealand.
North Shore Hospital, Auckland, New Zealand.
Int J Health Plann Manage. 2020 Nov;35(6):1593-1605. doi: 10.1002/hpm.3046. Epub 2020 Oct 14.
We present an elective surgery redesign project involving several New Zealand hospitals that is primarily data-driven. One of the project objectives is to improve the predictions of surgery durations. We address this task by considering two approaches: (a) linear regression modelling, and (b) improvement of the data quality. For (a) we evaluate the accuracy of predictions using two performance measures. These predictions are compared to the surgeons' estimates that may subsequently be adjusted. We demonstrate using the historical surgical lists that the estimates from our prediction techniques improve the scheduling of elective surgeries by minimising the occurrences of list under- and over-runs. For (b), we discuss how the surgical data motivates a review of the surgery procedure classification which takes into account the design of the electronic booking form. The proposed hierarchical classification streamlines the specification of surgery types and therefore retains the potential for improved predictions.
我们提出了一个涉及新西兰多家医院的选择性手术重新设计项目，该项目主要是数据驱动的。项目目标之一是提高手术持续时间的预测准确性。我们通过考虑两种方法来解决此任务：（a）线性回归建模，以及（b）提高数据质量。对于（a），我们使用两个性能指标评估预测的准确性。将这些预测与外科医生的估计进行比较，这些估计随后可能会进行调整。我们使用历史手术清单证明，我们的预测技术的估计通过最小化清单不足和过多的情况，改善了选择性手术的安排。对于（b），我们讨论了手术数据如何促使审查手术程序分类，这需要考虑电子预订表格的设计。拟议的分层分类简化了手术类型的指定，从而保持了提高预测的潜力。
