Institute of Systems Engineering, Dalian University of Technology, Dalian 116024, PR China.
Institute of Systems Engineering, Dalian University of Technology, Dalian 116024, PR China; School of Economics and Management, Tsinghua University, Beijing 100084, PR China.
J Biomed Inform. 2018 Jul;83:178-195. doi: 10.1016/j.jbi.2018.06.004. Epub 2018 Jun 15.
A clinical pathway (CP) defines a standardized care process for a well-defined patient group that aims to improve patient outcomes and promote patient safety. However, the construction of a new pathway from scratch is a time-consuming task for medical staff because it involves many factors, including objects, multidisciplinary collaboration, sequential design, and outcome measurements. Recently, the rapid development of hospital information systems has allowed the storage of large volumes of electronic medical records (EMRs), and this information constitutes an abundant data resource for building CPs using data-mining methods.
We provide an automatic method for extracting typical treatment processes from EMRs that consists of four key steps. First, a novel similarity method is proposed to measure the similarity of two treatment records. Then, we perform an affinity propagation (AP) clustering algorithm to cluster doctor order set sequences (DOSSs). Next, a framework is proposed to extract a high-level description of each treatment cluster. Finally, we evaluate the extracted typical treatment processes by matching the treatment cluster with external information, such as the treatment efficacy, length of stay, and treatment cost.
By experiments on EMRs of 8287 cerebral infarction patients, it is concluded that our proposed method can effectively extract typical treatment processes from treatment records, and also has great potential to improve treatment outcome by personalizing the treatment process for patients with different conditions.
The extracted typical treatment processes are intuitive and can provide managerial guidance for CP redesign and optimization. In addition, our work can assist clinicians in clearly understanding their routine treatment processes and recommend optimal treatment pathways for patients.
临床路径（CP）为明确界定的患者群体定义了标准化的护理流程，旨在改善患者的预后并提高患者安全性。然而，从头开始构建新的路径对医务人员来说是一项耗时的任务，因为它涉及许多因素，包括对象、多学科协作、顺序设计和结果测量。最近，医院信息系统的快速发展使得大量电子病历（EMR）得以存储，这些信息构成了使用数据挖掘方法构建 CP 的丰富数据资源。
我们提供了一种从 EMR 中提取典型治疗过程的自动方法，该方法包括四个关键步骤。首先，提出了一种新颖的相似性方法来衡量两个治疗记录的相似性。然后，我们执行亲和传播（AP）聚类算法对医嘱集序列（DOSS）进行聚类。接下来，提出了一个框架来提取每个治疗簇的高级描述。最后，通过将治疗簇与外部信息（如治疗效果、住院时间和治疗成本）进行匹配，评估提取的典型治疗过程。
通过对 8287 例脑梗死患者的 EMR 进行实验，得出结论：我们提出的方法可以有效地从治疗记录中提取典型的治疗过程，并且通过为不同病情的患者个性化治疗过程，具有改善治疗效果的巨大潜力。
提取的典型治疗过程直观，可以为 CP 的重新设计和优化提供管理指导。此外，我们的工作可以帮助临床医生清楚地了解他们的常规治疗过程，并为患者推荐最佳的治疗途径。
