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arXiv · 2601.14641

MIND: Empowering Mental Health Clinicians with Multimodal Data Insights through a Narrative Dashboard

Ruishi Zou·Shiyu Xu·Margaret E Morris·Jihan Ryu·Timothy D. Becker·Nicholas Allen·Anne Marie Albano·Randy Auerbach·Dan Adler·Varun Mishra·Lace Padilla·Dakuo Wang·Ryan Sultan·Xuhai "Orson" Xu
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Abstract

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.

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Ruishi Zou, Shiyu Xu, Margaret E Morris, Jihan Ryu, Timothy D. Becker, Nicholas Allen, Anne Marie Albano, Randy Auerbach, Dan Adler, Varun Mishra, Lace Padilla, Dakuo Wang, Ryan Sultan, Xuhai "Orson" Xu. 2026-01-21. MIND: Empowering Mental Health Clinicians with Multimodal Data Insights through a Narrative Dashboard. https://doi.org/10.1145/3772318.3790529

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