2025, Vol. 5, Issue 2, Part A

Author(s)
Feyikemi Mary Akinyelure

Abstract
The growing use of predictive analytics in healthcare is an opportunity and a critical challenge for mental health care delivery, particularly among marginalized communities. While predictive methods such as machine learning models, natural language processing, and risk stratification systems have shown potential for improving diagnostic, preventive, and treatment outcomes, their advantages are frequently unevenly distributed. The purpose of this review is to critically examine how predictive analytics can be effectively integrated with culturally competent care to close existing gaps in mental health treatment for marginalized communities. Findings from the review indicate that predictive analytics can improve clinical decision-making, enable early intervention, and customize treatment paths when developed, considering inclusivity. However, current predictive models frequently struggle with data bias, limited cultural adaptability, and insufficient community engagement, exacerbating rather than reducing inequities. Therefore, bridging the gap between predictive analytics and culturally competent mental health care is a technological and ethical imperative. This necessitates a multifaceted approach that combines inclusive data gathering, culturally conscious algorithm design, interdisciplinary collaboration, and strong policy monitoring.

DOI: https://doi.org/10.22271/27891623.2025.v5.i2a.75

Pages: 11-16 | Views: 93 | Downloads: 29