Yang Qian, Zimmerman John, Steinfeld Aaron, Carey Lisa, Antaki James F
School of Computer Science, Carnegie Mellon University, Pittsburgh PA, USA.
School of Biomedical Engineering, Carnegie Mellon University, Pittsburgh PA, USA.
ACM Trans Comput Hum Interact. 2016 May;2016:4477-4488. doi: 10.1145/2858036.2858373.
Clinical decision support tools (DSTs) are computational systems that aid healthcare decision-making. While effective in labs, almost all these systems failed when they moved into clinical practice. Healthcare researchers speculated it is most likely due to a lack of user-centered HCI considerations in the design of these systems. This paper describes a field study investigating how clinicians make a heart pump implant decision with a focus on how to best integrate an intelligent DST into their work process. Our findings reveal a lack of perceived need for and trust of machine intelligence, as well as many barriers to computer use at the point of clinical decision-making. These findings suggest an alternative perspective to the traditional use models, in which clinicians engage with DSTs at the point of making a decision. We identify situations across patients' healthcare trajectories when decision supports would help, and we discuss new forms it might take in these situations.
临床决策支持工具（DSTs）是辅助医疗保健决策的计算系统。虽然在实验室中有效，但几乎所有这些系统在进入临床实践时都失败了。医疗保健研究人员推测，这很可能是由于在这些系统的设计中缺乏以用户为中心的人机交互考虑因素。本文描述了一项实地研究，调查临床医生如何做出心脏泵植入决策，重点是如何最好地将智能DST集成到他们的工作流程中。我们的研究结果揭示了对机器智能缺乏感知需求和信任，以及在临床决策点使用计算机存在许多障碍。这些发现为传统的使用模式提供了另一种视角，在传统模式中，临床医生在决策点与DST进行交互。我们确定了在患者的医疗保健轨迹中决策支持会有帮助的情况，并讨论了在这些情况下它可能采取的新形式。
