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| Hauptverfasser: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Online-Zugang: | https://arxiv.org/abs/2601.14641 |
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| _version_ | 1866912837054496768 |
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| author | Zou, Ruishi Xu, Shiyu Morris, Margaret E Ryu, Jihan Becker, Timothy D. Allen, Nicholas Albano, Anne Marie Auerbach, Randy Adler, Dan Mishra, Varun Padilla, Lace Wang, Dakuo Sultan, Ryan Xu, Xuhai "Orson" |
| author_facet | Zou, Ruishi Xu, Shiyu Morris, Margaret E Ryu, Jihan Becker, Timothy D. Allen, Nicholas Albano, Anne Marie Auerbach, Randy Adler, Dan Mishra, Varun Padilla, Lace Wang, Dakuo Sultan, Ryan Xu, Xuhai "Orson" |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_14641 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MIND: Empowering Mental Health Clinicians with Multimodal Data Insights through a Narrative Dashboard Zou, Ruishi Xu, Shiyu Morris, Margaret E Ryu, Jihan Becker, Timothy D. Allen, Nicholas Albano, Anne Marie Auerbach, Randy Adler, Dan Mishra, Varun Padilla, Lace Wang, Dakuo Sultan, Ryan Xu, Xuhai "Orson" Human-Computer Interaction 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. |
| title | MIND: Empowering Mental Health Clinicians with Multimodal Data Insights through a Narrative Dashboard |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2601.14641 |