This chapter introduces a comprehensive feedback and treatment navigation system designed to support feedback-informed psychological therapy (FIT) and clinical training in cognitive-behavior therapy (CBT). Positioned within the framework of measurement-based care (MBC) and practice-based evidence (PBE), the Trier Treatment Navigator (TTN) integrates routine outcome monitoring (ROM) with advanced predictive tools to enhance data-informed decision-making in clinical practice. The system facilitates the identification of patients at risk of treatment failure through empirically derived expected treatment response (ETR) models and provides clinical support tools (CSTs) for personalized intervention adjustments. By aligning ongoing research with real-time clinical practice, the TTN exemplifies a learning healthcare system where practice and research mutually inform each other. The chapter outlines the implementation of the TTN at the University of Trier’s outpatient clinic and highlights its practical applications in training, supervision, and clinical practice. Key features of the TTN include its dynamic feedback mechanisms, therapist training integration, and predictive algorithms for treatment personalization and dropout risk. Results from a randomized controlled trial demonstrate the system’s efficacy in improving treatment outcomes and reducing dropout rates. Additionally, the chapter discusses recent advancements in precision mental health care, including the incorporation of ecological momentary assessment, machine learning, and natural language processing to refine the TTN’s predictive accuracy and scalability. This work underscores the importance of FIT systems like the TTN in bridging the research–practice gap, fostering evidence-based treatment strategies, and enabling personalized care. Future directions include expanding the system’s global implementation and enhancing therapist engagement to optimize the adoption of data-informed therapeutic practices.