Healthcare Professionals' Views on the Use of Passive Sensing and Machine Learning Approaches in Secondary Mental Healthcare: A Qualitative Study

Abstract

Introduction

Globally, many people experience mental health difficulties, and the current workforce capacity is insufficient to meet this demand, with growth not keeping pace with need. Digital devices that passively collect data and utilise machine learning to generate insights could enhance current mental health practices and help service users manage their mental health. However, little is known about mental healthcare professionals' perspectives on these approaches. This study aims to explore mental health professionals' views on using digital devices to passively collect data and apply machine learning in mental healthcare, as well as the potential barriers and facilitators to their implementation in practice.

Methods

Qualitative semi-structured interviews were conducted with 15 multidisciplinary staff who work in secondary mental health settings. Interview topics included the use of digital devices for passive sensing, developing machine learning algorithms from this data, the clinician's role, and the barriers and facilitators to their use in practice. Interview data were analysed using reflexive thematic analysis.

Results

Participants noted that digital devices for healthcare can motivate and empower users, but caution is needed to prevent feelings of abandonment and widening inequalities. Passive sensing can enhance assessment objectivity, but it raises concerns about privacy, data storage, consent and data accuracy. Machine learning algorithms may increase awareness of support needs, yet lack context, risking misdiagnosis. Barriers for service users include access, accessibility and the impact of receiving insights from passively collected data. For staff, barriers involve infrastructure and increased workload. Staff support facilitated service users' adoption of digital systems, while for staff, training, ease of use and feeling supported were key enablers.

Conclusions

Several recommendations have arisen from this study, including ensuring devices are user-friendly and equitably applied in clinical practice. Being with a blended approach to prevent service users from feeling abandoned and provide staff with training and access to technology to enhance uptake.

Patient or Public Contribution

The study design, protocol and topic guide were informed by a lived experience community group that advises on research projects at the authors' affiliation.