Evidence map›Paper›PMID 41987876›Full record

ArticleThe American journal of geriatric psychiatry. Open science, education, and practice2026

Designing AI-Enabled Video Monitoring Clinician Dashboard for Neuropsychiatric Symptoms: A Survey of User Needs.

Christine E Gould, Carter H Davis, Narayan Schüz, F Vankee Lin, Quincy M Samus, Tracy Terada, Merryn Daniel, Silvia Tee, Ehsan Adeli

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The American journal of geriatric psychiatry. Open science, education, and practice, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06704971 (Automatic Assessment of Neuropsychiatric Symptoms at Home Predictive of Mental Health and Cognitive Disorders Using Non-Intrusive Ambient Intelligence Technologies), which is not on this map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT06704971 active not recruitingnot on this map

Automatic Assessment of Neuropsychiatric Symptoms at Home Predictive of Mental Health and Cognitive Disorders Using Non-Intrusive Ambient Intelligence Technologies

TypeobservationalSponsorStanford UniversityRan2024 to 2026Enrolled25ConditionsMild Cognitive Impairment, Mild Dementia, Dementia Alzheimers, Dementia
3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Christine E GouldDepartment of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Palo Alto, CA.
Carter H DavisGeriatric Research, Education and Clinical Center (GRECC), VA Palo Alto Health Care System, Palo Alto, CA.
Narayan SchüzDepartment of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Palo Alto, CA.
F Vankee LinDepartment of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Palo Alto, CA.
Quincy M SamusJohns Hopkins School of Nursing, Baltimore, MD.
Tracy TeradaClinical Excellence Research Center (CERC), Stanford University School of Medicine, Palo Alto, CA.
Merryn DanielDepartment of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Palo Alto, CA.
Silvia TeeGeriatric Medicine Section, Division of Primary Care and Population Health, Stanford University School of Medicine, Palo Alto, CA.
Ehsan AdeliDepartment of Computer Science, Stanford University, Palo Alto, CA.

Funding

Utilizing Technology and AI Approaches to Facilitate Independence andResilience in Older AdultsP30AG073104 · NIA · JOHNS HOPKINS UNIVERSITY · PI Alexis Battle · 2021 to 2026
$31.2M
Neural mechanisms of gait disturbances as individualized digital biomarker trajectories in preclinical dementiaR01AG089169 · NIA · STANFORD UNIVERSITY · PI Ehsan Adeli · 2024 to 2026
$4.5M
U24 NEW Brain Aging Diversity SupplementU24AG072701 · NIA · UNIVERSITY OF ROCHESTER · PI CONWELL, YEATES, LIN, FENG VANKEE · 2021 to 2024
$2.6M
NIA NIH HHS P30 AG073104NIA NIH HHS R01 AG089169NIA NIH HHS U24 AG072701
6 · The paper itself

Abstract

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: 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.

Indexed as

Artificial intelligenceclinician dashboardhome monitoringneuropsychiatric symptomssurvey

Identifiers

PMID41987876
PMCPMC13076236

What OpenQuestion holds

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.