The impact of AI-driven sentiment analysis on patient outcomes in psychiatric care: A narrative review

Abstract

This article addresses the pressing question of how advanced analytical tools, specifically artificial intelligence (AI)-driven sentiment analysis, can be effectively integrated into psychiatric care to enhance patient outcomes. Utilizing specific search phrases like “AI-driven sentiment analysis,” “psychiatric care,” and “patient outcomes,” a comprehensive survey of English-language publications from the years 2014–2024 was performed. This examination encompassed multiple databases such as PubMed, PsycINFO, Google Scholar, and IEEE Xplore. Through a comprehensive analysis of qualitative case studies and quantitative metrics, the study uncovered that the implementation of sentiment analysis significantly improves clinicians’ ability to monitor and respond to patient emotions, leading to more tailored treatment plans and increased patient engagement. Key findings indicated that sentiment analysis improves early mood disorder detection, personalizes treatments, enhances patient-provider communication, and boosts treatment adherence, leading to better mental health outcomes. The significance of these findings lies in their potential to revolutionize psychiatric care by providing healthcare professionals with real-time insights into patient feelings and responses, thereby facilitating more proactive and empathetic care strategies. Furthermore, this study highlights the broader implications for healthcare systems, suggesting that the incorporation of sentiment analysis can lead to a paradigm shift in how mental health services are delivered, ultimately enhancing the efficacy and quality of care. By addressing barriers to new technology adoption and demonstrating its practical benefits, this research contributes vital knowledge to the ongoing discourse on optimizing healthcare delivery through innovative solutions in psychiatric settings.