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

TL;DR

MIND is a narrative dashboard powered by large language models that helps mental health clinicians interpret multimodal patient data more effectively, improving decision-making and revealing hidden insights.

Contribution

The paper introduces MIND, a novel LLM-powered dashboard that presents multimodal mental health data through narratives and charts, co-designed with clinicians.

Findings

Clinicians found MIND significantly improved data insight discovery (p<.001).

MIND supported clinical decision-making (p=.004).

Participants perceived MIND as a substantial improvement over baseline methods.

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.