Umbrella Review: BI, Data Analytics, and ML in Mental Health and Wellbeing Services

This protocol outlines the methodology for an umbrella review that synthesises existing systematic reviews and other evidence syntheses on the application of data-driven approaches within mental health and wellbeing services. Specifically, the review focuses on data analytics, business intelligence (BI), data visualisation, and machine learning (ML) techniques as applied to understanding client needs and improving service delivery. An umbrella review approach was selected because the underlying primary literature has been the subject of multiple recent reviews of varying scope, methodology, and quality. Synthesising these reviews provides a higher-order map of what is currently known, where evidence converges or conflicts, and where gaps remain. The review will follow the Joanna Briggs Institute (JBI) methodology for umbrella reviews (Aromataris et al., 2015; JBI Manual for Evidence Synthesis, Chapter 10) and report findings in line with the PRISMA 2020 statement. Searches were executed on 27 January 2026 in Scopus, Web of Science, IEEE Xplore, ACM Digital Library, and PsycINFO, restricted to peer-reviewed English-language literature published between 2021 and 2026. Two reviewers will independently screen titles, abstracts, and full texts, and will independently appraise included reviews using the JBI Critical Appraisal Checklist for Systematic Reviews and Research Syntheses. Primary-study overlap across included reviews will be assessed using the Corrected Covered Area (CCA) method (Pieper et al., 2014). Findings will be synthesised narratively and presented through summary tables, evidence maps, and thematic groupings. Ethical approval is not required. Findings will be disseminated through registration on the Open Science Framework and submission to a peer-reviewed journal or conference.