TY  - JOUR
AU  - Rogan, Jessica
AU  - Bucci, Sandra
AU  - Firth, Joseph
PY  - 2024
DA  - 2024/1/23
TI  - Health Care Professionals’ Views on the Use of Passive Sensing, AI, and Machine Learning in Mental Health Care: Systematic Review With Meta-Synthesis
JO  - JMIR Ment Health
SP  - e49577
VL  - 11
KW  - artificial intelligence
KW  - machine learning
KW  - passive sensing
KW  - mental health care
KW  - clinicians
KW  - views
KW  - meta-synthesis
KW  - review
KW  - mental health
KW  - health care
KW  - health care professionals
KW  - psychology
KW  - psychiatry
KW  - mental health professionals
KW  - mobile phone
AB  - Background: Mental health difficulties are highly prevalent worldwide. Passive sensing technologies and applied artificial intelligence (AI) methods can provide an innovative means of supporting the management of mental health problems and enhancing the quality of care. However, the views of stakeholders are important in understanding the potential barriers to and facilitators of their implementation. Objective: This study aims to review, critically appraise, and synthesize qualitative findings relating to the views of mental health care professionals on the use of passive sensing and AI in mental health care. Methods: A systematic search of qualitative studies was performed using 4 databases. A meta-synthesis approach was used, whereby studies were analyzed using an inductive thematic analysis approach within a critical realist epistemological framework. Results: Overall, 10 studies met the eligibility criteria. The 3 main themes were uses of passive sensing and AI in clinical practice, barriers to and facilitators of use in practice, and consequences for service users. A total of 5 subthemes were identified: barriers, facilitators, empowerment, risk to well-being, and data privacy and protection issues. Conclusions: Although clinicians are open-minded about the use of passive sensing and AI in mental health care, important factors to consider are service user well-being, clinician workloads, and therapeutic relationships. Service users and clinicians must be involved in the development of digital technologies and systems to ensure ease of use. The development of, and training in, clear policies and guidelines on the use of passive sensing and AI in mental health care, including risk management and data security procedures, will also be key to facilitating clinician engagement. The means for clinicians and service users to provide feedback on how the use of passive sensing and AI in practice is being received should also be considered. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42022331698; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=331698 
SN  - 2368-7959
UR  - https://mental.jmir.org/2024/1/e49577
UR  - https://doi.org/10.2196/49577
UR  - http://www.ncbi.nlm.nih.gov/pubmed/38261403
DO  - 10.2196/49577
ID  - info:doi/10.2196/49577
ER  -