Muro Naiara, Larburu Nekane, Bouaud Jacques, Seroussi Brigitte
Sorbonne Université, Université Paris 13 Sorbonne Paris Cité, INSERM UMR_S 1142, LIMICS, Paris, France.
eHeatlh and Biomedical Applications, Vicomtech, Donostia-San Sebastian, Spain.
Stud Health Technol Inform. 2019 Jul 4;262:134-137. doi: 10.3233/SHTI190035.
Clinical Practice Guidelines (CPGs) gather latest evidence-based results to guide and support clinicians over the decision-making process to provide best care. Nevertheless, clinical cases may be subject to some biases (understood as non-compliance with CPGs) that can lead to adapt care delivery. In this work an experience-based decision support leaning on the structuration of the Decisional Event concept for tracking and storing each clinical decision is presented. Moreover, a visual analytics tool is provided in order to facilitate the visualization of biases from guideline-based decision support and the identification and inclusion of real-world evidence into the reasoning process by augmenting the knowledge formalized in the implemented guidelines.
临床实践指南(CPGs)收集最新的循证结果,以指导和支持临床医生进行决策,从而提供最佳护理。然而,临床病例可能存在一些偏差(即不符合CPGs),这可能导致调整护理方式。在这项工作中,我们提出了一种基于经验的决策支持方法,该方法依赖于决策事件概念的结构化,用于跟踪和存储每个临床决策。此外,还提供了一种可视化分析工具,以便于可视化基于指南的决策支持中的偏差,并通过增强实施指南中形式化的知识,将真实世界的证据识别并纳入推理过程。