School of Health Sciences, Central Michigan University, Mt. Pleasant, MI, USA.
BMC Med Res Methodol. 2018 Nov 16;18(1):137. doi: 10.1186/s12874-018-0587-6.
Clinical Decision Support Systems (CDSS) provide aid in clinical decision making and therefore need to take into consideration human, data interactions, and cognitive functions of clinical decision makers. The objective of this paper is to introduce a high level reference model that is intended to be used as a foundation to design successful and contextually relevant CDSS systems. The paper begins by introducing the information flow, use, and sharing characteristics in a hospital setting, and then it outlines the referential context for the model, which are clinical decisions in a hospital setting. Important characteristics of the Clinical decision making process include: (i) Temporally ordered steps, each leading to new data, which in turn becomes useful for a new decision, (ii) Feedback loops where acquisition of new data improves certainty and generates new questions to examine, (iii) Combining different kinds of clinical data for decision making, (iv) Reusing the same data in two or more different decisions, and (v) Clinical decisions requiring human cognitive skills and knowledge, to process the available information. These characteristics form the foundation to delineate important considerations of Clinical Decision Support Systems design. The model includes six interacting and interconnected elements, which formulate the high-level reference model (CDSS-RM). These elements are introduced in the form of questions, as considerations, and are examined with the use of illustrated scenario-based and data-driven examples. The six elements /considerations of the reference model are: (i) Do CDSS mimic the cognitive process of clinical decision makers? (ii) Do CDSS provide recommendations with longitudinal insight? (iii) Is the model performance contextually realistic? (iv) Is the 'Historical Decision' bias taken into consideration in CDSS design? (v) Do CDSS integrate established clinical standards and protocols? (vi) Do CDSS utilize unstructured data? The CDSS-RM reference model can contribute to optimized design of modeling methodologies, in order to improve response of health systems to clinical decision-making challenges.
临床决策支持系统（CDSS）为临床决策提供辅助，因此需要考虑临床决策者的人机交互、数据交互和认知功能。本文的目的是介绍一个高级参考模型，旨在作为设计成功且与上下文相关的 CDSS 系统的基础。本文首先介绍了医院环境中的信息流、使用和共享特征，然后概述了模型的参考上下文，即医院环境中的临床决策。临床决策过程的重要特征包括：（i）时间顺序的步骤，每个步骤都导致新的数据，而新的数据反过来又可用于新的决策；（ii）反馈循环，在该循环中，新数据的获取可提高确定性并生成新的问题以供检查；（iii）组合用于决策的不同类型的临床数据；（iv）在两个或更多不同的决策中重复使用相同的数据；（v）临床决策需要人类认知技能和知识，以处理可用信息。这些特征构成了描绘临床决策支持系统设计重要考虑因素的基础。该模型包括六个相互作用和相互关联的元素，构成了高级参考模型（CDSS-RM）。这些元素以问题的形式提出，作为考虑因素，并使用基于场景和基于数据的示例进行检查。参考模型的六个元素/考虑因素是：（i）CDSS 是否模仿临床决策者的认知过程？（ii）CDSS 是否提供具有纵向洞察力的建议？（iii）模型性能是否符合上下文实际情况？（iv）CDSS 设计是否考虑了“历史决策”偏差？（v）CDSS 是否集成了既定的临床标准和协议？（vi）CDSS 是否利用非结构化数据？CDSS-RM 参考模型可以为优化建模方法的设计做出贡献，以提高卫生系统对临床决策挑战的应对能力。
