(Paper in progress)

Background: SimGID is a generative AI-powered platform designed to support a Community of Practice (CoP) for nurses in Catalonia. Through the creation of synthetic cases, SimGID enhances practical training, addresses diverse healthcare scenarios, and fosters professional collaboration. Its key components include synthetic profiles, situational dynamics, and interactions with the healthcare system, allowing for comprehensive and realistic simulations.

Objective: The primary goal of SimGID is to improve nurse training by simulating complex healthcare scenarios. It achieves this by providing an interactive platform where users create, share, and evaluate synthetic cases tailored to practical needs. The platform integrates advanced AI technologies to ensure realistic and adaptable case generation.

Key Features:

• Synthetic Cases: Divided into three categories:
  • Person Profiles: Fictional individuals reflecting real-world healthcare demographics and their social ties.
  • Life Situations: Contexts affecting health, such as pregnancy or loss.
  • Healthcare Interactions: Scenarios ranging from primary care visits to emergency situations.
• User Management: Secure access for administrators and contributors with varied authority levels.
• Case Management Tools: AI-assisted interfaces for creating and refining synthetic cases, complete with timelines and related documentation.

Development Progress:

1. Beta Release: The SimGID Knowledge Base (SimGID KB) platform is now in beta testing, enabling early user feedback and refinement.
2. Case Generation: Existing cases are structured and utilized for generative AI testing, focusing on Google Gemini and OpenAI models.
3. Community Engagement: Dashboard metrics and user contributions are actively tracked to promote collaboration and ensure quality.

Implementation in Nursing Practice: SimGID is operational in pilot phases, emphasizing practical applicability in nurse training. Nurses can explore case libraries, create new scenarios, and analyze outcomes, fostering a hands-on learning environment.

Next Steps: Following successful beta testing and positive reception, SimGID is being expanded to other healthcare specialties. This phase aims to validate its scalability and adapt its functionalities to diverse clinical and training needs.

Keywords: SimGID, Generative AI, Nurse Training, Community of Practice, Healthcare Simulation. For more information contact [email protected]