JMIR Publications

ABSTRACT

Background:

Continuity of care is essential in psychiatric services due to the chronic, relapsing nature of mental health conditions, yet care pathways remain heavily fragmented at critical transition interfaces. While advancements in artificial intelligence (AI) and machine learning (ML) offer powerful capabilities to track longitudinal data and automate clinical decision-making, a structured appraisal of their efficacy in psychiatric care continuity is lacking. This protocol outlines a mixed-methods systematic review to evaluate how AI-driven workflows can proactively augment monitoring, triage care resources, and bridge systemic coordination gaps.

Objective:

The primary objective of this systematic review is to evaluate the effectiveness of artificial intelligence (AI) and machine learning (ML) interventions in psychiatric care settings for improving the continuity of patient care. Secondary objectives include stratifying the types of AI architectures used and identifying implementation barriers and facilitators.

Methods:

A systematic literature search across MEDLINE, Embase, CENTRAL, CINAHL, and APA PsycInfo will be conducted for peer-reviewed randomized controlled trials, non-randomized interventional studies, and qualitative or mixed-methods evaluations published between January 1, 2016, and December 31, 2025. Two independent reviewers will perform study screening, data extraction, and quality assessments. Mixed-methods convergent synthesis utilizing the Joanna Briggs Institute (JBI) convergent segregated approach will be carried out to synthesize the quantitative efficacy and qualitative implementation data.

Results:

This review is self-funded and was officially registered with PROSPERO on January 24, 2026 (CRD420251245352). Comprehensive database searches have commenced, with full-text screening and transcript reviews projected to conclude by late August 2026, ahead of data extraction and a targeted systematic review manuscript submission in early Spring 2027.

Conclusions:

By systematically mapping interventions across patient, institutional, and health system levels, this review will clarify the clinical effectiveness, ethical boundaries, and logistical implementation factors of psychiatric AI tools. Ultimately, these consolidated insights will provide an evidence-based foundation to inform clinical guidelines, governance frameworks, and the design of proactive, learning mental health systems. Clinical Trial: Registered with PROSPERO on January 24, 2026 (CRD420251245352).