Behind the Therapy: The Path to Better Mental Health Communication

At Virtus, our journey with Neuro AI is guided by a unique and impactful approach to learning and problem-solving known as Challenge-Based Learning (CBL).

What Is Challenge-Based Learning (CBL)?

Diagram illustrating Challenge-Based Learning (CBL) Methodology

• Engage:

  The "Engage" phase is about sparking interest and understanding the context of the challenge. In Neuro AI, this phase involves immersing the team in the real-world issues of patient-psychologist communication. We begin by exploring the barriers and difficulties patients and therapists face during interactions, such as emotional misinterpretation or difficulty in expressing feelings. This phase sets the foundation for the project by engaging stakeholders like mental health professionals, patients, and data scientists, ensuring everyone is aligned and motivated to solve the problem.

  
  

• Investigate:

  In the "Investigate" phase, we gather information and conduct research to understand the problem more deeply. For Neuro AI, this means analyzing existing communication methods, exploring how brainwave data and emotional cues can improve interactions, and studying the current state of AI in mental health care. We dive into data collection, including brainwave signals and emotional patterns, and assess how AI could be applied to enhance communication. During this phase, we engage with experts in AI, psychology, and neuroscience to ensure we understand all aspects of the challenge and explore innovative solutions.

• Act:

  The "Act" phase is where the solution is developed and implemented. For Neuro AI, this involves designing and testing AI models that can analyze brainwave data and emotional cues to facilitate better communication between patients and psychologists. We build prototypes, run trials, and collect feedback to refine our approach. This phase also includes taking action based on the insights gathered during the investigation phase, ensuring the solution is practical, effective, and scalable. We continue to improve and adjust the AI model based on real-world usage, ensuring it meets the needs of both patients and therapists.