More Modality, More AI: Exploring Design Opportunities of AI-Based Multi-modal Remote Monitoring Technologies for Early Detection of Mental Health Sequelae in Youth Concussion Patients

TL;DR

The paper addresses the challenge of early detection of mental health sequelae in youth after concussion by introducing AI-RPM for remote monitoring. Through a three-stage process—formative study, design of AI-RPM interfaces, and design evaluation with concussion clinicians—it derives design considerations and delivers a refined clinician-facing dashboard integrating wearables, LLM-based conversational agents, and AI risk prediction. Key contributions include identifying workflow challenges, proposing a multi-modal AI-RPM design with data provenance, and demonstrating how such a system can improve communication, provide richer insights, and support timely clinical decisions. The work advances human-centered AI in RPM, highlighting practical design priorities, potential benefits, and limitations for real-world clinical adoption. It lays a foundation for expanding AI-RPM approaches to other clinical contexts requiring remote mental health monitoring and decision support.

Abstract

Anxiety, depression, and suicidality are common mental health sequelae following concussion in youth patients, often exacerbating concussion symptoms and prolonging recovery. Despite the critical need for early detection of these mental health symptoms, clinicians often face challenges in accurately collecting patients' mental health data and making clinical decision-making in a timely manner. Today's remote patient monitoring (RPM) technologies offer opportunities to objectively monitor patients' activities, but they were not specifically designed for youth concussion patients; moreover, the large amount of data collected by RPM technologies may also impose significant workloads on clinicians to keep up with and use the data. To address these gaps, we employed a three-stage study consisting of a formative study, interface design, and design evaluation. We first conducted a formative study through semi-structured interviews with six highly professional concussion clinicians and identified clinicians' key challenges in remotely collecting patient information and accessing patient treatment compliance. Subsequently, we proposed preliminary clinician-facing interface designs with the integration of AI-based RPM technologies (AI-RPM), followed by design evaluation sessions with highly professional concussion clinicians. Clinicians underscored the value of integrating multi-modal AI-RPM technologies to support their decision-making while emphasizing the importance of customizable interfaces through collaborative design and multiple responsible design considerations.

Figures (1)

• Figure 1: The preliminary system design based on concussion clinicians' challenges and needs: (A) AI risk score prediction for mental health sequelae, (B) patient self-report symptoms, (C) patient sleep and physical activities.