Redesigning psychiatric case management with artificial intelligence: Opportunities and limitations in client follow-up and resource coordination

The integration of Artificial Intelligence (AI) into mental health services has opened new possibilities for enhancing psychiatric case management, especially in client follow-up and resource coordination. This study explores how AI technologies such as predictive analytics, natural language processing, and intelligent scheduling systems can transform traditional case management practices. By examining current models used in psychiatric social work and comparing them with AI-assisted frameworks, this research aims to identify areas where AI can streamline processes, improve continuity of care, and optimize referral and resource allocation.

While AI shows promise in automating routine follow-up reminders, monitoring treatment adherence, and predicting client risk factors using historical data, limitations such as data privacy concerns, algorithmic bias, and ethical dilemmas remain significant. Through qualitative interviews with psychiatric social workers and a review of case studies using AI tools in mental health contexts, this paper identifies best practices and key barriers to implementation. A hybrid model is proposed, where AI complements but does not replace the human judgment essential to therapeutic alliances and personalized care.

The findings suggest that while AI can significantly reduce administrative burden and enhance coordination efficiency, it requires careful integration with ethical safeguards and constant human oversight. This paper contributes to a deeper understanding of how technology can support, rather than supplant, the relational core of psychiatric social work.