Anxiety, depression, and suicidality are common mental health sequelae following concussion in youth patients, often exacerbating concussion symptoms and prolonging recovery. Despite the critical need for early detection of these mental health symptoms, clinicians often face challenges in accurately collecting patients' mental health data and making clinical decision-making in a timely manner. Today's remote patient monitoring (RPM) technologies offer opportunities to objectively monitor patients' activities, but they were not specifically designed for youth concussion patients; moreover, the large amount of data collected by RPM technologies may also impose significant workloads on clinicians to keep up with and use the data. To address these gaps, we employed a three-stage study consisting of a formative study, interface design, and design evaluation. We first conducted a formative study through semi-structured interviews with six highly professional concussion clinicians and identified clinicians' key challenges in remotely collecting patient information and accessing patient treatment compliance. Subsequently, we proposed preliminary clinician-facing interface designs with the integration of AI-based RPM technologies (AI-RPM), followed by design evaluation sessions with highly professional concussion clinicians. Clinicians underscored the value of integrating multi-modal AI-RPM technologies to support clinicians' decision-making while emphasizing the importance of customizable interfaces with explainability and multiple responsible design considerations.

翻译：焦虑、抑郁和自杀倾向是青少年脑震荡后常见的心理健康后遗症，往往会加剧脑震荡症状并延长恢复时间。尽管早期检测这些心理健康症状至关重要，但临床医生在准确收集患者心理健康数据并及时做出临床决策方面常常面临挑战。当前的远程患者监测技术为客观监测患者活动提供了可能，但这些技术并非专门为青少年脑震荡患者设计；此外，远程监测技术收集的大量数据也可能给临床医生带来巨大的工作负担，难以跟上并有效利用这些数据。为弥补这些不足，我们开展了一项包含形成性研究、界面设计和设计评估的三阶段研究。我们首先通过对六位高度专业的脑震荡临床医生进行半结构化访谈开展形成性研究，识别了临床医生在远程收集患者信息和获取患者治疗依从性方面面临的主要挑战。随后，我们提出了集成基于AI的远程监测技术的初步临床医生界面设计方案，并邀请高度专业的脑震荡临床医生进行设计评估。临床医生强调了整合多模态AI远程监测技术对支持临床决策的价值，同时强调了具有可解释性及多重负责任设计考量的可定制界面的重要性。