Accepted for/Published in: JMIR Mental Health
Date Submitted: Jun 2, 2023
Date Accepted: Nov 1, 2023
Healthcare Professionals’ Views on the use of Passive Sensing, Artificial Intelligence and Machine Learning in Mental Healthcare: Meta-Synthesis
ABSTRACT
Background:
Mental health difficulties are highly prevalent worldwide. A shortage of mental healthcare workers means it is unlikely that practice based solely on in-person care will ever be able to meet mental healthcare demand. The use of digital tools to support mental healthcare delivery may offer one solution to this problem. Passive sensing technologies and applied artificial intelligence (AI) methods could provide an innovative means of supporting management of mental health problems and enhancing quality of care. However, the views of stakeholders are important in understanding potential barriers/facilitators to their implementation.
Objective:
The aim of this study was to review, critically appraise and synthesise qualitative findings relating to the views of mental healthcare professionals on the use of passive sensing and AI in mental healthcare.
Methods:
A systematic search of qualitative papers was undertaken using four databases. A meta-synthesis approach was used, whereby studies were analysed using an inductive thematic analysis approach, within a critical realist epistemological framework.
Results:
Overall, 10 papers met eligibility criteria. Three main themes were: (1) use of passive sensing and AI in clinical practice; (2) barriers and facilitators to use in practice; and (3) consequences for service users. Five sub-themes were also identified: barriers; facilitators; empowerment; risk to wellbeing; and data privacy and protection issues.
Conclusions:
Whilst clinicians are open-minded to the use of passive sensing and AI in mental healthcare, important factors to consider are service user wellbeing, clinician workloads and the therapeutic relationship. Service users and clinicians need to 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 around use of passive sensing and AI in mental healthcare, including risk management and data security procedures, will also be key to facilitate clinician engagement. Means for clinicians and service users to feedback how the use of passive sensing and AI in practice is being received should also be considered.
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.