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Designing AI-Enabled Video Monitoring Clinician Dashboard for Neuropsychiatric Symptoms: A Survey of User Needs
Christine E Gould1,2, Carter H Davis2, Narayan Schüz1
1Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Palo Alto, CA.
Objective:
This study aimed to gather input from clinicians who assess and treat neuropsychiatric symptoms (NPS) to inform the development of a clinician dashboard to accompany an AI-enabled video-based monitoring system.
Methods:
The clinician survey inquired about the importance of tracking different NPS and about additional information or features desired for the dashboard. Responses (n = 28) were grouped into prescribing and nonprescribing clinicians for sensitivity analyses.
Results:
The most important NPS to be detected were agitation/aggression, nighttime behaviors, depression, and anxiety. Multiple environmental factors were endorsed as being very important including: behavior frequency, intensity, and time of day.
Conclusions:
Findings demonstrate that the desired features of the dashboard were consistent across both prescribing and nonprescribing clinicians. Notably, some of the important symptoms and features that clinicians desired in a dashboard could not be extracted from existing sensor-based systems, but would be possible with an AI-enabled video monitoring system.
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