Evidence map›Paper›PMID 42475737›Full record

ArticleJMIR human factors2026

Video-Algorithmic Patient Monitoring in Mental Health Inpatient Settings: Qualitative Study of Patient or Consumer, Clinician, and Vendor Perspectives.

Piers Gooding, Hamilton Kennedy, Simon D'Alfonso, Timothy Kariotis, Catherine Daniel, Bridget Hamilton

Abstract read
In one paragraph

Article in JMIR human factors, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

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

6 authors.

Piers GoodingLa Trobe Law School, La Trobe University, Bundoora, Melbourne, 3086, Australia, 61 3 9479 1846.ORCID 0000-0001-5743-5708
Hamilton KennedyCentre for Mental Health Nursing, The University of Melbourne, Melbourne, VIC, Australia.ORCID 0000-0001-8144-0208
Simon D'AlfonsoSchool of Computing & Information Systems, The University of Melbourne, Melbourne, Australia.ORCID 0000-0001-7407-8730
Timothy KariotisThe University of Melbourne, Melbourne, Australia.ORCID 0000-0001-7190-7564
Catherine DanielSchool of Nursing, Midwifery and Paramedicine, Australian Catholic University, Melbourne, Victoria, Australia.ORCID 0000-0002-1122-3950
Bridget HamiltonCentre for Mental Health Nursing, The University of Melbourne, Melbourne, VIC, Australia.ORCID 0000-0001-8711-7559

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Video-algorithmic patient monitoring (VAPM) combines remote, noncontact sensors and algorithmic analysis and is increasingly trialed in acute psychiatric and other care settings. While promoted for improving safety and reducing risk, it raises ethical concerns regarding safety, privacy and surveillance. Little is known about how those encountering VAPM in mental health care contexts anticipate its use and potential impacts, including where it has not yet been implemented. Objective: This study aimed to explore the views of patients or mental health consumers, specialized mental health nurses and nurse academics, hospital managers, and technology vendors regarding the appropriateness and anticipated implications of VAPM in mental health inpatient care. Methods: This qualitative study identified key stakeholders in Australia via networking techniques for participation in a deliberative workshop. A deliberative workshop was held, and the workshop discussion was audio-recorded, transcribed, and thematically analyzed, consistent with methods in health technology research, which enable exploration of different viewpoints, including convergences and divergences across stakeholder groups. Results: In total, 16 stakeholders participated, exploring themes concerning (1) contestation over the rationale for VAPM in mental health settings, (2) VAPM reshaping care and relationships, (3) perceived harms of VAPM, (4) perceived observational support for safety and reduced disruption, (5) serious privacy implications of VAPM, (6) the need for appropriate governance, and (7) the potential for VAPM to transform, not augment, service delivery. General views differed across groups. Patients or service users expressed concerns about privacy, coercion, and the potential to intensify stigma. Mental health nurses were cautious but interested in possible benefits for safety and suicide prevention. Hospital managers and technology vendors largely emphasized safety gains. Conclusions: The findings suggest that the anticipated risks of VAPM are primarily experienced subjectively, as infringements on privacy, dignity, and trust, while purported benefits remain largely untested and unquantified. From a utilitarian perspective, direct comparison is therefore difficult-the risks are set out in the anticipated experiences of those with lived experience, and the benefits remain hypothetical. From this view, robust, independent evidence of real-world outcomes is required. Yet, for some participants, the very premise of such calculation was rejected, with privacy, dignity, and trust regarded as nonnegotiable, rather than items for trade-off. If VAPM is to be pursued at all, it should proceed only with extreme caution, with transparent evidence of outcomes, and with meaningful participation from those whose lives and care are most directly impacted.

Indexed as

Attitude of Health PersonnelInpatientsAustraliaHumansQualitative ResearchRemote Patient Monitoringhealth and artificial intelligencemonitoring and surveillance in mental health settingspassive remote monitoring technologyvideo-algorithmic patient monitoringvision-based patient monitoring and management

Identifiers

PMID42475737
PMCPMC13384473

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Registered trials

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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.