Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, South Korea.
Department of Obstetrics and Gynecology, Seoul National University Bundang Hospital, Seongnam, South Korea.
Int J Med Inform. 2020 Jan;133:104015. doi: 10.1016/j.ijmedinf.2019.104015. Epub 2019 Oct 16.
A clinical pathway is one of the tools used to support clinical decision making that provides a standardized care process in a specific context. The objective of this research was to develop a method for building data-driven clinical pathways using electronic health record data.
We proposed a matching rate-based clinical pathway mining algorithm that produces the optimal set of clinical orders for each clinical stage by employing matching rates. To validate the approach, we utilized two different datasets of deidentified inpatient records directly related to total laparoscopic hysterectomy (TLH) and rotator cuff tears (RCTs) from a hospital in South Korea. The derived data-driven clinical pathways were evaluated with knowledge-based models by health professionals using a delta analysis.
Two different data-driven clinical pathways, i.e., TLH and RCTs, were produced by applying the matching rate-based clinical pathway mining algorithm. We identified that there were significant differences in clinical orders between the data-driven and knowledge-based models. Additionally, the data-driven clinical pathways based on our algorithm outperformed the models by clinical experts, with average matching rates of 82.02% and 79.66%, respectively.
The proposed algorithm will be helpful for supporting clinical decisions and directly applicable in medical practices.
临床路径是支持临床决策的工具之一，它为特定情况下提供了标准化的护理流程。本研究旨在开发一种使用电子健康记录数据构建数据驱动的临床路径的方法。
我们提出了一种基于匹配率的临床路径挖掘算法，该算法通过使用匹配率为每个临床阶段生成最佳的临床医嘱集。为了验证该方法，我们使用了来自韩国一家医院的直接与腹腔镜全子宫切除术（TLH）和肩袖撕裂（RCT）相关的两个不同的匿名住院记录数据集。通过使用基于知识的模型，由医疗保健专业人员使用增量分析对推导得出的数据驱动临床路径进行评估。
通过应用基于匹配率的临床路径挖掘算法，生成了两种不同的数据驱动临床路径，即 TLH 和 RCT。我们发现数据驱动和基于知识的模型之间的临床医嘱存在显著差异。此外，基于我们算法的临床路径在平均匹配率方面优于临床专家模型，分别为 82.02%和 79.66%。
所提出的算法将有助于支持临床决策，并可直接应用于医疗实践。
