Current clinical decision support systems (CDSSs) typically base their predictions on correlation, not causation. In recent years, causal machine learning (ML) has emerged as a promising way to improve decision-making with CDSSs by offering interpretable, treatment-specific reasoning. However, existing research often emphasizes model development rather than designing clinician-facing interfaces. To address this gap, we investigated how CDSSs based on causal ML should be designed to effectively support collaborative clinical decision-making. Using a design science research methodology, we conducted a structured literature review and interviewed experienced physicians. From these, we derived eight empirically grounded design requirements, developed seven design principles, and proposed nine practical design features. Our results establish guidance for designing CDSSs that deliver causal insights, integrate seamlessly into clinical workflows, and support trust, usability, and human-AI collaboration. We also reveal tensions around automation, responsibility, and regulation, highlighting the need for an adaptive certification process for ML-based medical products.

翻译：当前临床决策支持系统（CDSSs）通常基于相关性而非因果关系进行预测。近年来，因果机器学习（ML）通过提供可解释、针对特定治疗的推理，已成为提升CDSS决策质量的有效途径。然而，现有研究多聚焦模型开发，而非面向临床医师的交互界面设计。为填补这一空白，我们探究了基于因果ML的CDSS应如何设计以有效支持协作式临床决策。采用设计科学研究方法，我们进行了结构化文献综述并访谈了资深医师。由此提炼出八项基于实证的设计需求，制定了七项设计原则，并提出九项可操作性设计特征。本研究为设计能传递因果洞见、无缝融入临床工作流、并支持可信度、可用性及人机协作的CDSS提供了指导。同时揭示了自动化、责任归属及监管之间的张力，凸显建立基于ML的医疗产品适应性认证流程的必要性。