Advances in data collection enable the capture of rich patient-generated data: from passive sensing (e.g., wearables and smartphones) to active self-reports (e.g., cross-sectional surveys and ecological momentary assessments). Although prior research has demonstrated the utility of patient-generated data in mental healthcare, significant challenges remain in effectively presenting these data streams along with clinical data (e.g., clinical notes) for clinical decision-making. Through co-design sessions with five clinicians, we propose MIND, a large language model-powered dashboard designed to present clinically relevant multimodal data insights for mental healthcare. MIND presents multimodal insights through narrative text, complemented by charts communicating underlying data. Our user study (N=16) demonstrates that clinicians perceive MIND as a significant improvement over baseline methods, reporting improved performance to reveal hidden and clinically relevant data insights (p<.001) and support their decision-making (p=.004). Grounded in the study results, we discuss future research opportunities to integrate data narratives in broader clinical practices.

翻译：数据采集技术的进步使得捕获丰富的患者生成数据成为可能：从被动感知（如可穿戴设备和智能手机）到主动自我报告（如横断面调查和生态瞬时评估）。尽管先前研究已证明患者生成数据在心理健康护理中的实用性，但在将这些数据流与临床数据（如临床记录）有效整合以支持临床决策方面，仍存在重大挑战。通过与五位临床医生进行协同设计，我们提出了MIND，这是一个由大型语言模型驱动的仪表盘，旨在为心理健康护理提供临床相关的多模态数据洞察。MIND通过叙事文本呈现多模态洞察，并辅以传达底层数据的图表。我们的用户研究（N=16）表明，临床医生认为MIND相较于基线方法有显著改进，报告其在揭示隐藏的、临床相关的数据洞察（p<.001）和支持其决策制定（p=.004）方面表现更优。基于研究结果，我们讨论了将数据叙事整合到更广泛临床实践中的未来研究机遇。