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.
@article{arxiv.2601.14641,
title = {MIND: Empowering Mental Health Clinicians with Multimodal Data Insights through a Narrative Dashboard},
author = {Ruishi Zou and Shiyu Xu and Margaret E Morris and Jihan Ryu and Timothy D. Becker and Nicholas Allen and Anne Marie Albano and Randy Auerbach and Dan Adler and Varun Mishra and Lace Padilla and Dakuo Wang and Ryan Sultan and Xuhai "Orson" Xu},
journal= {arXiv preprint arXiv:2601.14641},
year = {2026}
}
Comments
Conditionally accepted to CHI Conference on Human Factors in Computing Systems (CHI'26)