For over a decade, the National Aeronautics and Space Administration (NASA) has tracked and configuration-managed approximately 30 risks that affect astronaut health and performance before, during and after spaceflight. The Human System Risk Board (HSRB) at NASA Johnson Space Center is responsible for setting the official risk posture for each of the human system risks and determining—based on evaluation of the available evidence—when that risk posture changes. The ultimate purpose of tracking and researching these risks is to find ways to reduce spaceflight-induced risk to astronauts. The adverse effects of spaceflight begin at launch and continue throughout the duration of the mission, and in some cases, across the lifetime of the astronaut. Historically, research has been conducted in individual risk “silos” to characterize risk, however, astronauts are exposed to all risks simultaneously. In January of 2020, the HSRB at NASA began assessing the potential value of causal diagramming as a tool to facilitate understanding of the complex causes and effects that contribute to spaceflight-induced human system risk. Causal diagrams in the form of directed acyclic graphs (DAGs) are used to provide HSRB stakeholders with a shared mental model of the causal flow of risk. While primarily improving communication among those stakeholders, DAGs also allow a composite risk network to be created that can be tracked and configuration managed. This paper outlines the HSRB’s pilot process for this effort, the lessons learned, and future goals for data-driven risk management approaches.

十多年来，美国国家航空航天局 (NASA) 已跟踪并配置管理了大约 30 种影响宇航员在航天之前、之中和之后的健康和表现的风险。NASA 约翰逊航天中心的人类系统风险委员会 (HSRB) 负责为每个人类系统风险制定官方风险态势，并根据对现有证据的评估来确定风险态势何时发生变化。跟踪和研究这些风险的最终目的是找到减少航天飞行对宇航员造成的风险的方法。太空飞行的不利影响从发射时就开始，并持续到整个任务期间，在某些情况下，甚至持续到宇航员的一生。从历史上看，研究是在个人风险“孤岛”中进行的，以描述风险的特征，然而，宇航员同时面临所有风险。2020 年 1 月，NASA 的 HSRB 开始评估因果图作为工具的潜在价值，以促进理解导致航天引起的人体系统风险的复杂原因和影响。有向无环图 (DAG) 形式的因果图用于为 HSRB 利益相关者提供风险因果流的共享心理模型。在主要改善这些利益相关者之间的沟通的同时，DAG 还允许创建可跟踪和配置管理的复合风险网络。本文概述了 HSRB 的试点流程、经验教训以及数据驱动风险管理方法的未来目标。