This study reports the findings of qualitative interview sessions conducted with ICU clinicians for the co-design of a system user interface of an artificial intelligence (AI)-driven clinical decision support (CDS) system. This system integrates medical record data with wearable sensor, video, and environmental data into a real-time dynamic model that quantifies patients' risk of clinical decompensation and risk of developing delirium, providing actionable alerts to augment clinical decision-making in the ICU setting. Co-design sessions were conducted as semi-structured focus groups and interviews with ICU clinicians, including physicians, mid-level practitioners, and nurses. Study participants were asked about their perceptions on AI-CDS systems, their system preferences, and were asked to provide feedback on the current user interface prototype. Session transcripts were qualitatively analyzed to identify key themes related to system utility, interface design features, alert preferences, and implementation considerations. Ten clinicians participated in eight sessions. The analysis identified five themes: (1) AI's computational utility, (2) workflow optimization, (3) effects on patient care, (4) technical considerations, and (5) implementation considerations. Clinicians valued the CDS system's multi-modal continuous monitoring and AI's capacity to process large volumes of data in real-time to identify patient risk factors and suggest action items. Participants underscored the system's unique value in detecting delirium and promoting non-pharmacological delirium prevention measures. The actionability and intuitive interpretation of the presented information was emphasized. ICU clinicians recognize the potential of an AI-driven CDS system for ICU delirium and acuity to improve patient outcomes and clinical workflows.

翻译：本研究报告了与重症监护室（ICU）临床医护人员进行定性访谈会议的结果，旨在协同设计一款人工智能（AI）驱动的临床决策支持（CDS）系统的用户界面。该系统将电子病历数据与可穿戴传感器、视频和环境数据整合到一个实时动态模型中，量化患者临床失代偿和谵妄发生的风险，并提供可操作的警报，以增强ICU环境中的临床决策能力。协同设计会议以半结构化焦点小组和访谈形式与ICU临床医护人员（包括医师、中级执业者和护士）共同开展。研究参与者被问及他们对AI-CDS系统的看法、系统偏好，并被要求对当前用户界面原型提供反馈。会议记录经过定性分析，以识别与系统效用、界面设计特征、警报偏好及实施考量相关的关键主题。十位临床医护人员参与了八场会议。分析确定了五个主题：（1）AI的计算效用，（2）工作流程优化，（3）对患者护理的影响，（4）技术考量，以及（5）实施考量。临床医护人员重视CDS系统的多模态连续监测能力，以及AI实时处理大量数据以识别患者风险因素并建议行动项目的能力。参与者强调了该系统在检测谵妄和促进非药物性谵妄预防措施方面的独特价值。他们着重指出了所呈现信息的可操作性和直观解读性。ICU临床医护人员认识到AI驱动的CDS系统在改善ICU谵妄与病情危重度管理方面的潜力，有望提升患者预后和临床工作流程。