Gkegkas, Charis (2025). AI adoption in mental healthcare documentation practice. Independent thesis, Advanced level (Master’s degree, one year), 15 HE credits. Stockholm University, Faculty of Social Sciences, Stockholm Business School.

Abstract

Background: Artificial Intelligence (AI) holds significant potential to transform mental healthcare. This thesis investigates how mental healthcare professionals perceive and experience the integration of an AI-based “clinical co-pilot” tool designed to support clinical documentation within their daily routines. Understanding how clinicians perceive and integrate AI tools into their workflows is critical for successful implementation and sustainable digital transformation.

Methods: This study employed a sequential explanatory mixed-methods design. A quantitative survey was completed by 93 mental healthcare professionals to assess attitudes toward an AI-based clinical documentation tool. This was followed by semi-structured interviews with 8 unit-level managers, exploring the factors that influence AI adoption and integration in clinical practice. The study drew on multiple theoretical perspectives, including systemic and organizational-level theories, professional and workplace-level theories, individual behavioral theories, and cognitive and psychological theories. This comprehensive, multi-theoretical approach facilitated a deep exploration of the various factors influencing AI adoption.

Results: The findings revealed that healthcare professionals' willingness to recommend the AI tool was strongly associated with perceptions of practical utility, educational support, and preserved clinical autonomy. Significant variability in AI adoption was observed across different professional roles, age, and prior experience, with local leadership, organizational culture, and clinical context emerging as key factors. 

Business Insights: The study provides actionable recommendations for organizations seeking to enhance AI adoption. These include developing comprehensive training programs and maintaining clinicians' professional autonomy, while carefully balancing individual adaptation with organizational cohesion. Empowering leadership and fostering a strong organizational culture at all levels are also essential. In addition,establishing a clear governance structure and ongoing monitoring of key performance indicators (KPIs) are critical for sustained AI adoption.

Conclusion: Successful AI adoption in mental healthcare is not solely a technical challenge but a complex process shaped by human, organizational, and contextual factors. By understanding these dynamics, organizations can develop more effective strategies for integrating AI into clinical workflows, ensuring both improved patient care and optimized operational efficiency.