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：当前的临床决策支持系统（CDSSs）通常基于相关性而非因果性做出预测。近年来，因果机器学习（ML）通过提供可解释的、基于治疗的推理，成为改善CDSS决策质量的有前途方法。然而，现有研究往往侧重于模型开发，而非设计面向临床医生的交互界面。为弥补这一空白，我们研究了如何设计基于因果ML的CDSS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经验丰富的医师。据此，我们推导出八项以经验为基础的设计需求，制定了七项设计原则，并提出了九项实用设计特征。我们的研究结果为设计能传递因果洞见、无缝融入临床工作流程、支持信任、可用性及人机协作的CDSS提供了指导。同时，我们揭示了自动化、责任与监管方面的张力，凸显了对基于ML的医疗产品进行适应性认证流程的需求。