发表机构
Swansea University; EMRTS, Swansea Bay UHB（斯旺西大学; EMRTS，斯旺西湾大学健康保健董事会）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过民族志观察分析院前急救调度流程，识别出三个关键决策步骤，并发现第一步最适合引入安全有效的算法辅助，以改善临床医生工作负担和患者结局。
AI 中文摘要
本研究采用民族志沉浸与观察作为情境探究方法，以理解急救医疗调度危重症中心的情境行动。这项工作是对在设计过程中纳入情境特定知识与参与的迫切需求的回应，旨在缩小“AI鸿沟”——即人工智能（AI）系统的承诺与其为临床医生和患者实际带来的成果之间的差距。本文描述并分析了院前危重症团队调度过程的工作，揭示了工作流程的结构以及调度决策任务对工作人员提出的不同认知需求。为理解“实际工作”并识别过程中的众多依赖关系，本研究提炼了人员执行此项工作时在注意力、沟通和专注方面的要素。其动机在于确定AI支持可能在何处有用，并发现设计适当算法辅助时可能面临的挑战。我们探讨是否、在何处以及如何考虑AI系统的设计与实施。最终目标在于改进决策过程本身，以惠及临床医生和患者。研究识别了情境工作流程中的三个关键步骤，并详细说明了决策者在紧急呼叫沿复杂路径流转时如何逐一应对这些步骤。我们发现有力证据表明，这些决策步骤中的第一步构成了最有望实现无干扰辅助的候选环节，此类辅助可能安全有效地改善临床医生工作负担和临床结局。
英文摘要
This study uses ethnographic immersion and observation as contextual inquiry to understand situated action at an Emergency Medical Dispatch critical care hub. The work is a response to the urgent need to recruit context-specific knowledge and participation into design that helps to narrow the AI Chasm - the gap between the promise of Artificial Intelligence (AI) systems and what they deliver for clinicians and their patients. The work of a pre-hospital critical care team's dispatch process is described and analysed to reveal both the structure of the workflow and the different cognitive demands it makes on staff tasked with dispatch decision-making. Elements of attention, communication and focus between the humans, as they carry out this work, are drawn out in order to understand the work-as-done and identify the many dependencies in the process. The motivation is to establish where AI support might be useful and to discover what challenges there could be in designing appropriate algorithmic assistance. We ask whether, where and how the design and implementation of an AI system might be considered. The ultimate objective is to improve the decision process itself to the benefit of clinicians and patients. The study identifies three key steps in the situated workflow and details how decision-makers negotiate each one as emergency calls follow complex routes between them. We find compelling evidence that the first of these decision steps constitutes the most promising candidate for unobtrusive assistance that could be safe and effective in improving both clinician workload and clinical outcomes.
Comments49 pages, 11 figures
