Healthcare Professionals' views on the use of Passive Sensing and Artificial Intelligence in Mental Healthcare

Abstract

Mental health services are facing pressures worldwide, such as high demand and understaffing. Digital approaches to mental healthcare could offer solutions to such challenges. In particular, 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. The views of stakeholders are important in understanding potential barriers and facilitators to their implementation. This thesis aimed to explore healthcare professionals' views on the use of passive sensing and AI in mental healthcare. Paper one reports a meta-synthesis, which reviewed, critically appraised and synthesised qualitative findings relating to the views of mental healthcare professionals on the use of passive sensing and AI in mental healthcare. A systematic search of the literature resulted in the identification of ten eligible studies. Clinical implications of the findings and recommendations for future research are discussed. Paper two explores mental health professionals' perspectives on the use of passive sensing and artificial intelligence, specifically machine learning methods, in mental healthcare. This included specific discussion around barriers and facilitators to use in practice. Fifteen clinical staff who work in secondary mental health settings were interviewed. Thematic analysis was used to analyse the data. Participants recognised a number of positives and negatives around the use of digital devices, passive sensing and machine learning in mental healthcare, as well as individual, systemic and organisational barriers to use in practice. Clinical implications are discussed and recommendations were developed for services to consider when implementing digitally-supported mental healthcare. Paper three provides a critical evaluation, reflecting on the planning, implementation and interpretation of both papers. Overall clinical implications of the thesis, recommendations for future research and personal reflections on the research process are provided.

Date of Award	31 Dec 2023
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Sandra Bucci (Supervisor) & Joseph Firth (Supervisor)